Bridge reconstruction method and computer equipment

Abstract

The present application relates to a bypass reconstruction method and computer equipment. The method includes: obtaining heart segmentation results and blood vessel segmentation results, obtaining a target blood vessel mask based on the heart segmentation results, blood vessel segmentation results and segmentation model, obtaining bypass reconstruction information based on the target blood vessel mask and blood vessel segmentation results, and processing the bypass reconstruction information to obtain a bypass reconstruction result. The method can obtain bypass reconstruction information through a neural network model, and obtain a bypass reconstruction result through processing the bypass reconstruction information. This process avoids manual reconstruction of the bypass model, saves human resources and bypass reconstruction time, reduces bypass reconstruction costs, and further improves bypass reconstruction efficiency.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a bypass reconstruction method and computer equipment. Background Art

[0002] At present, cardiovascular disease has become a common disease, and the incidence and mortality rate remain high. Among them, coronary heart disease is particularly serious, greatly affecting people's daily life. Coronary artery bypass grafting is a commonly used and most effective surgical method for treating coronary heart disease. Therefore, regular follow-up of patients who have undergone bypass grafting is also an essential process.

[0003] In traditional technology, the bypass data is usually reconstructed manually after surgery so that doctors can understand the postoperative effect. However, the traditional method will result in low efficiency of bypass reconstruction. Summary of the invention

[0004] Based on this, it is necessary to provide a bridge reconstruction method and computer equipment to address the above technical problems.

[0005] A bridge reconstruction method, the method comprising:

[0006] Obtain heart segmentation results and blood vessel segmentation results;

[0007] Acquire a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0008] Obtaining bypass reconstruction information according to the target vessel mask;

[0009] The bridging reconstruction information is processed to obtain a bridging reconstruction result.

[0010] A bridge reconstruction method, the method comprising:

[0011] Obtain heart segmentation results and blood vessel segmentation results;

[0012] Acquire a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0013] Based on the target blood vessel mask and the blood vessel segmentation result, obtaining bypass reconstruction information, the bypass reconstruction information including a bypass starting point and an anastomosis between the bypass and a normal blood vessel;

[0014] The bridging reconstruction information is processed to obtain a bridging reconstruction result.

[0015] A bridge reconstruction method, the method comprising:

[0016] Obtain heart segmentation results and blood vessel segmentation results;

[0017] Acquire a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0018] Based on the target blood vessel mask and the blood vessel segmentation result, obtaining bypass reconstruction information, the bypass reconstruction information including a bypass path and an anastomosis between the bypass and a normal blood vessel;

[0019] The bridging reconstruction information is processed to obtain a bridging reconstruction result.

[0020] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0021] Obtain heart segmentation results and blood vessel segmentation results;

[0022] Acquire a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0023] Based on the target blood vessel mask and the blood vessel segmentation result, obtaining bypass reconstruction information, the bypass reconstruction information including a bypass starting point and an anastomosis between the bypass and a normal blood vessel;

[0024] The bridging reconstruction information is processed to obtain a bridging reconstruction result.

[0025] The above-mentioned bypass reconstruction method and computer equipment, the method includes obtaining heart segmentation results and blood vessel segmentation results, obtaining a target blood vessel mask based on the heart segmentation results, blood vessel segmentation results and segmentation model, and obtaining bypass reconstruction information based on the target blood vessel mask and blood vessel segmentation results. The bypass reconstruction information includes the bypass starting point and the anastomosis between the bypass and the normal blood vessel, and the bypass reconstruction result is obtained by processing the bypass reconstruction information; the above-mentioned method obtains the bypass reconstruction information through a neural network model, and obtains the bypass reconstruction result by processing the bypass reconstruction information. This process avoids manual reconstruction of the bypass model, saves human resources and bypass reconstruction time, reduces the cost of bypass reconstruction, and further improves the efficiency of bypass reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0027] Figure 2 A schematic diagram of a flow chart of a bridge reconstruction method in one embodiment;

[0028] Figure 3 A schematic diagram of a method flow for obtaining a target blood vessel mask in one embodiment;

[0029] Figure 4A schematic flow chart of a method for obtaining an initial blood vessel mask in another embodiment;

[0030] Figure 5 It is a schematic diagram of a specific method flow for obtaining a target blood vessel mask in another embodiment;

[0031] Figure 6 It is a flowchart of a method for determining whether a first blood vessel segmentation result includes bypass data in another embodiment;

[0032] Figure 7 It is a flowchart of a specific method for determining whether a first blood vessel segmentation result includes bypass data in another embodiment;

[0033] Figure 7a It is a schematic diagram of the process result of executing the bypass reconstruction method on the cardiac image of a diagnosis and treatment object in another embodiment;

[0034] Figure 8 A schematic diagram of a flow chart of a bridge reconstruction method in one embodiment;

[0035] Fig. 9 A schematic diagram of a method flow for obtaining bridge reconstruction information in one embodiment;

[0036] Fig.10 A schematic diagram of a method flow for determining a candidate bridge point in one embodiment;

[0037] Fig.11 A schematic flow chart of a method for determining a bridging starting point in another embodiment;

[0038] Fig.12 It is a schematic flow chart of a specific method for determining a bridging starting point in another embodiment;

[0039] Fig.13 A schematic flow chart of a specific method for obtaining bridge reconstruction information in another embodiment;

[0040] Fig.14 A schematic diagram of a flow chart of a bridge reconstruction method in one embodiment;

[0041] Fig.15 A schematic diagram of a method flow for obtaining bridge reconstruction information in one embodiment;

[0042] Fig.16 A schematic diagram of a method flow chart for determining a bridge path trajectory in one embodiment;

[0043] Fig.17 A schematic flow chart of a method for determining a bridge end point in one embodiment;

[0044] Fig.18is a distribution structure diagram of multiple blood vessel segmentation points in a trajectory diagram in another embodiment;

[0045] Fig.19 A schematic flow chart of a method for determining a bridge end point in another embodiment;

[0046] Fig. 20 A schematic flow chart of a specific method for obtaining bridge reconstruction information in another embodiment;

[0047] Fig.21 FIG. 4 is a structural block diagram of a bridge reconstruction device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The bridge reconstruction method provided in this application can be applied to Figure 1 Computer equipment as shown. Figure 1 As shown, the computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store cardiac images, blood vessel segmentation results, and pre-trained segmentation models. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a bypass reconstruction method is implemented.

[0050] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0051] It should be noted that the bridging reconstruction method provided in the embodiment of the present application may be performed by a bridging reconstruction device, which may be implemented as part or all of a computer device through software, hardware, or a combination of software and hardware. The following method embodiments are described using a computer device as an example.

[0052] Among them, in possible implementations, the above-mentioned bypass reconstruction method can be implemented in various ways. Specifically, several bypass reconstruction methods can be further described in detail through the technical solutions in the following embodiments, but are not limited to the following embodiments.

[0053] The first bridge reconstruction method

[0054] The bridge reconstruction method can be described in detail by the following embodiments:

[0055] like Figure 2 FIG. 1 is a flow chart of a bridge reconstruction method provided by an embodiment, wherein the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0056] S1000: Obtain heart segmentation results and blood vessel segmentation results.

[0057] Specifically, the computer device can input multiple frames of cardiac images of the diagnosis and treatment object into a pre-trained cardiac segmentation model to obtain a cardiac segmentation result. Further, the vascular range is determined by the cardiac segmentation result, and the vascular range is segmented to obtain a vascular segmentation result. Optionally, the vascular segmentation result can be a segmentation result corresponding to all vascular regions in the cardiac segmentation result. The above-mentioned cardiac image can be a three-dimensional CT enhanced image in dicom format. The above-mentioned cardiac segmentation result can be a cardiac segmentation image containing each chamber of the heart, the aorta, and the aortic arch, and each chamber of the heart, the aorta, and the aortic arch in the cardiac segmentation image have corresponding labels to distinguish different tissues in the cardiac segmentation image. The various chambers of the heart can be the left atrium, right atrium, left ventricle and right ventricle of the heart, wherein the labels corresponding to the left atrium, right atrium, left ventricle, right ventricle, aorta, and aortic arch of the heart can be different. In addition, the above-mentioned cardiac segmentation result can also be represented by cardiac segmentation data containing each chamber of the heart, the aorta, and the aortic arch.

[0058] It can be understood that the above-mentioned pre-trained heart segmentation model can be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model; wherein, the computer device can perform network model training on the initial heart segmentation model through a heart image training set to obtain a pre-trained heart segmentation model.

[0059] At the same time, the heart segmentation result can be taken as a whole to determine the blood vessel range. Among them, the computer device can extend the heart segmentation result outward by a certain preset range to obtain the blood vessel range, and further, input the blood vessel range into a pre-trained blood vessel segmentation model to obtain the blood vessel segmentation result. The above preset range can be customized according to actual conditions, as long as the blood vessel range is smaller than the range where the heart image is located. If the heart segmentation result is represented by a heart segmentation image, the blood vessel range can be an image representation obtained by extending the heart segmentation image outward by a certain preset range; if the heart segmentation result is represented by heart segmentation data, the blood vessel range can be a heart segmentation data representation corresponding to the image obtained by extending the heart segmentation image outward by a certain preset range. The above blood vessel segmentation result can be a binary blood vessel segmentation result.

[0060] The pre-trained blood vessel segmentation model may be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model. If the pre-trained blood vessel segmentation model and the pre-trained heart segmentation model have the same structure, the network parameters of the two may be different after network model training. The computer device may perform network model training on the initial blood vessel segmentation model using a blood vessel range training set to obtain a pre-trained blood vessel segmentation model.

[0061] In addition, both the heart segmentation model and the blood vessel segmentation model can complete network model training before executing S100 in this embodiment.

[0062] S1100 , obtaining a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model.

[0063] Specifically, the computer device may process the heart segmentation result and the blood vessel segmentation result to obtain a processing result, and input the processing result into the segmentation model to obtain a multi-vessel mask corresponding to each point in the blood vessel segmentation result, i.e., a target blood vessel mask, or directly input the heart segmentation result and the blood vessel segmentation result into the segmentation model to obtain a multi-vessel mask corresponding to each point in the blood vessel segmentation result, i.e., a target blood vessel mask. In this embodiment, the heart segmentation result and the blood vessel segmentation result may be understood as images or data.

[0064] It is understandable that the above segmentation model can be a pre-trained neural network model, and the segmentation model can be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model. If the pre-trained segmentation model has the same structure as the pre-trained vascular segmentation model and the pre-trained heart segmentation model, the network parameters of the three can be different after the network model training. In addition, the above segmentation model can complete the network model training before the execution of S200 in this embodiment. Specifically, the computer device can input the heart segmentation result, the heart segmentation result in the vascular segmentation result training set, and the vascular segmentation result into the initial segmentation model to obtain a vascular prediction mask, calculate the prediction error value between the vascular prediction mask and the standard vascular mask through the loss function, and update the initial network parameters in the initial segmentation model according to the prediction error value, and continuously iterate the above training steps until the prediction error value meets the preset error threshold or the number of iterations reaches the preset number of iterations threshold, so as to obtain a pre-trained segmentation model. The above standard vascular mask can be an idealized vascular mask corresponding to the entire vascular, that is, the gold standard for network training.

[0065] S1200: Obtain bypass reconstruction information according to the target blood vessel mask.

[0066] Specifically, the computer device can perform transformation processing, comparison processing, calculation processing, screening processing and / or analysis processing on the target blood vessel mask to obtain the bypass reconstruction information, or it can also first screen the target bypass data from the target blood vessel mask according to the preset conditions, and analyze the target bypass data to determine the bypass reconstruction information. The above-mentioned target blood vessel mask can be understood as a multi-vessel label mask corresponding to the blood vessel segmentation result. The above-mentioned bypass reconstruction information can include at least one of the bypass path, the range of the bypass path, the bypass start point and the bypass end point in the bypass path, the size of the bypass path, and each passing point in the bypass path.

[0067] S1300: Process the bridging reconstruction information to obtain a bridging reconstruction result.

[0068] Specifically, the computer device can process the bypass reconstruction information to obtain a three-dimensional heart bypass model diagram, that is, the bypass reconstruction result. In addition, the computer device can first process the normal blood vessels in sections to obtain the centerline information of all blood vessels, and then process the centerline information of all blood vessels and the bypass reconstruction information to obtain the bypass reconstruction result. Furthermore, the computer device can output the bypass reconstruction result and display it to the doctor for viewing, so that the doctor can make regular return visits to the diagnosis and treatment object according to the bypass reconstruction result.

[0069] In the above-mentioned bypass reconstruction method, the computer device can obtain the heart segmentation result and the blood vessel segmentation result, obtain the target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model, obtain the bypass reconstruction information according to the target blood vessel mask, and obtain the bypass reconstruction result through processing through the bypass reconstruction information; this method obtains the bypass reconstruction information through a neural network model, and determines the bypass reconstruction result through reconstruction of the bypass reconstruction information, which can avoid manual reconstruction of the bypass model, save human resources and bypass reconstruction time, reduce the bypass reconstruction cost, and further improve the bypass reconstruction efficiency; at the same time, this method realizes bypass reconstruction through a neural network model, which can avoid reconstruction errors caused by manual participation in the reconstruction process, thereby improving the accuracy of bypass reconstruction.

[0070] As one of the embodiments, Figure 3 As shown, the blood vessel segmentation result includes the first blood vessel segmentation result, and the segmentation model includes the first segmentation model. The step of acquiring the target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model in the above S1100 can be implemented by the following steps:

[0071] S1110 . Determine a distance field using a heart mask, where the distance field includes a distance field of a heart chamber and a distance field of an aortic arch.

[0072] Specifically, the heart mask may include a heart chamber mask and an aorta and aortic arch mask, and the heart mask, the heart chamber mask and the aorta and aortic arch mask are all the same size as the heart segmentation image. The computer device may generate a heart chamber mask according to the labels corresponding to the left ventricle and the right ventricle of the heart in the heart segmentation image, and the pixel values ​​corresponding to the left ventricle and the right ventricle region of the heart in the heart chamber mask may be 1, and the pixel values ​​of the remaining regions may be 0. At the same time, the computer device may also generate an aorta and aortic arch mask according to the labels corresponding to the aorta and the aortic arch in the heart segmentation image, and the pixel values ​​corresponding to the aorta and the aortic arch region of the heart in the generated aorta and aortic arch mask may be 1, and the pixel values ​​of the remaining regions may be 0.

[0073] It should be noted that the left ventricle and the right ventricle of the heart in the heart chamber mask can be regarded as a whole and can be called the left and right ventricles; the aorta and the aortic arch in the aortic arch mask can be regarded as a whole and can be called the aortic arch. Further, the computer device can use the Euclidean distance, Manhattan distance, cosine distance, Minkowski distance or Chebyshev distance calculation method to calculate the distance between each point in the other areas of the heart chamber mask except the area where the left and right ventricles are located and the nearest point on the surface of the left and right ventricles, and use all the calculated distances as the distance field of the heart chamber. At the same time, the computer device can also use the Euclidean distance, Manhattan distance, cosine distance, Minkowski distance or Chebyshev distance calculation method to calculate the distance between each point in the other areas of the aortic arch mask except the area where the aortic arch is located and the nearest point on the surface of the aortic arch, and use all the calculated distances as the distance field of the aortic arch.

[0074] S1120 , obtaining an initial blood vessel mask through the heart segmentation result, the distance field, the first blood vessel segmentation result, and the first segmentation model.

[0075] Specifically, the computer device can process the heart segmentation result and the first blood vessel segmentation result, and then input the processing result and the distance field into the first segmentation model for processing to obtain an initial blood vessel mask. Alternatively, the computer device can directly input the heart segmentation result, the distance field, and the first blood vessel segmentation result into the first segmentation model to obtain an initial blood vessel mask. The initial blood vessel mask is a part of the target blood vessel mask, and the target blood vessel mask and the initial blood vessel mask may include a blood vessel mask and a bypass mask. The above-mentioned first blood vessel segmentation result can be a segmentation result corresponding to a partial blood vessel area in the heart segmentation result, or it can be a segmentation result in which the blood vessel range is only in the vicinity of the heart's chamber; the distance field input into the first segmentation model can be the distance field of the heart chamber and / or the distance field of the aorta and aortic arch.

[0076] It should be noted that the computer device can perform network model training on the initial first segmentation model through the distance field of the heart chamber and the distance field training set of the aorta and aortic arch, the heart segmentation result and the blood vessel segmentation result training set to obtain a pre-trained first segmentation model. Specifically, the computer device can input the distance field of the heart chamber and the distance field of the aorta and aortic arch in the training set, the heart segmentation result and the blood vessel segmentation result corresponding to the blood vessel area around the heart chamber in the training set, and the heart segmentation result and the blood vessel segmentation result in the training set into the initial first segmentation model. The initial first segmentation model First, the heart segmentation result and the vascular segmentation result are mapped and merged to obtain a merged segmentation result, and then each point in the vascular area in the merged segmentation result is voted to obtain a multi-vascular prediction mask, and the prediction error value between the multi-vascular prediction mask and the standard multi-vascular mask is calculated by the loss function, and the initial network parameters in the initial first segmentation model are updated according to the prediction error value, and the above training steps are continuously iterated until the prediction error value meets the preset error threshold or the number of iterations reaches the preset iteration number threshold, so as to obtain the pre-trained first segmentation model; wherein, when the first segmentation model is used, the first segmentation model finally outputs the initial vascular mask.

[0077] In addition, when the network model is trained for the initial first segmentation model, the images in the training set of the heart segmentation results and the corresponding blood vessel segmentation results can be images of a size between 64*64*64 and 256*256*256. In this embodiment, the image size can be selected as 128*128*128, and the image resolution can be selected as 0.6mm~2.0mm. In this embodiment, the image resolution can be 1.2mm. During the network model training process, the minimization of loss functions such as cross entropy, focal loss and / or diceloss can be used as the optimization goal, and parameter optimization methods such as Adam, sgd, AdamW, RMSprop, etc. can be used to achieve optimization processing. The above-mentioned standard multi-vessel mask can be an idealized multi-vessel mask. The structure of the first segmentation model and the normal blood vessel segmentation model can be the same.

[0078] Among them, this embodiment uses the distance field of the heart chamber and the distance field of the aorta and aortic arch, which can help the first segmentation model determine the relative position of the blood vessel in the heart or aorta, and play an auxiliary role in improving the segmentation of the blood vessel. The first segmentation model can include four input channels and 17 output channels, the four input channels are respectively a heart segmentation result input channel, a first blood vessel segmentation result input channel, a heart chamber distance field input channel, and an aortic aortic arch distance field input channel, wherein the 17 output channels can respectively output 15 types of blood vessel masks, bypass masks, and background masks corresponding to non-vascular areas in the heart segmentation results.

[0079] S1130: Determine a target blood vessel mask according to the initial blood vessel mask.

[0080] Specifically, the computer device may perform expansion processing on the initial vascular mask to obtain the target vascular mask, or perform processing on the initial vascular mask, the heart segmentation result, the vascular segmentation result and the segmentation model to obtain the target vascular mask.

[0081] The above-mentioned bypass reconstruction method can determine the distance field through the heart mask, and the distance field includes the distance field of the heart chamber and the distance field of the aorta and the aortic arch. The initial vascular mask is obtained through the heart segmentation result, the distance field, the first vascular segmentation result and the first segmentation model. According to the initial vascular mask, the target vascular mask is determined; the method obtains the target vascular mask of the entire vascular area through the neural network model, thereby improving the accuracy of the obtained vascular mask and shortening the determination time of the vascular mask.

[0082] As one of the embodiments, Figure 4 As shown, the step of obtaining an initial blood vessel mask by using the heart segmentation result, the distance field, the first blood vessel segmentation result and the first segmentation model in the above S1120 includes:

[0083] S1121 , mapping and merging the dirty segmentation result and the first blood vessel segmentation result to obtain a merged segmentation result.

[0084] In this embodiment, the computer device may map each tissue in the heart segmentation result with the corresponding tissue in the first blood vessel segmentation result, and merge the heart segmentation result with the mapped corresponding first blood vessel segmentation result to obtain a merged segmentation result.

[0085] S1122: Input the distance field and the merged segmentation results into the first segmentation model to obtain an initial blood vessel mask.

[0086] Furthermore, the computer device may input the distance field and the merged segmentation result into the first segment model for processing to obtain an initial blood vessel mask, and the distance field may include the distance field of the heart chamber and the distance field of the aorta and aortic arch.

[0087] It is understandable that the above-mentioned initial vascular mask or target vascular mask may include at least one vascular mask and a bypass mask. The vascular mask may be any one of 15 vascular masks, namely, right coronary artery (RCA) mask, right posterior descending coronary artery (R-PDA) mask, left ventricular right posterior branch (R-PLB) mask, left main coronary artery (LM) mask, left anterior descending branch (LAD) mask, diagonal branch (D) mask, circumflex branch (LCX) mask, obtuse marginal branch (OM) mask, left posterior descending branch (L-PDA) mask, left ventricular left posterior branch (L-PLB) mask, intermediate branch (RAMUS) mask, atrial branch (LACX) mask, sharp marginal branch (AM) mask, anterior septal branch (S) mask or sinoatrial node branch (LSN) mask.

[0088] The above-mentioned bypass reconstruction method can map and merge the heart segmentation result and the first blood vessel segmentation result to obtain a merged segmentation result, and input the distance field and the merged segmentation result into the first segmentation model to obtain an initial blood vessel mask, and then determine the target blood vessel mask based on the initial blood vessel mask; this method obtains the blood vessel mask of the blood vessel area through a neural network model, thereby improving the accuracy of the obtained blood vessel mask and shortening the time for determining the blood vessel mask.

[0089] As one of the embodiments, the blood vessel segmentation result includes a second blood vessel segmentation result, and the segmentation model includes a second segmentation model; as shown in FIG. Figure 5 As shown, the step of determining the target blood vessel mask according to the initial blood vessel mask in the above S1130 can be implemented by the following steps:

[0090] S1131. Determine whether the first blood vessel segmentation result includes bridging data according to the initial blood vessel mask.

[0091] Specifically, the computer device can determine all first vessel segmentation results belonging to the bypass mask in the merged segmentation result according to the initial vessel mask corresponding to the vessel region in the merged segmentation result, and then determine whether the first vessel segmentation result contains bypass data, i.e., bypass data, according to the number of all first vessel segmentation results belonging to the bypass mask. All first vessel segmentation results belonging to the bypass mask can be equal to all first vessel segmentation results in the merged segmentation result, or can be equal to some of the first vessel segmentation results in the merged segmentation result, or there is no first vessel segmentation result belonging to the bypass mask in the merged segmentation result. That is, the number of all first vessel segmentation results belonging to the bypass mask can be equal to 0, or can be greater than or equal to 1.

[0092] S1132: If the first blood vessel segmentation result includes bypass data, obtain the remaining blood vessel segmentation results within the blood vessel range to obtain a second blood vessel segmentation result.

[0093] Specifically, when the computer device determines that the first blood vessel segmentation result includes the bypass data, the remaining blood vessel segmentation results within the blood vessel range can be obtained to obtain a second blood vessel segmentation result. The second blood vessel segmentation result corresponds to the blood vessel region included in the region above the heart in the heart segmentation result, that is, the result after the first blood vessel segmentation result and the second blood vessel segmentation result are combined can be equal to the image or data of the entire blood vessel region in the heart segmentation result.

[0094] S1133 , input the heart segmentation result, the distance field, the first blood vessel segmentation result, and the second blood vessel segmentation result into the second segmentation model to obtain a target blood vessel mask.

[0095] Specifically, the computer device can perform computational processing on the heart segmentation result, the first blood vessel segmentation result, and the second blood vessel segmentation result, and then input the computational processing result and the distance field into the second segmentation model for processing to obtain a target blood vessel mask. Alternatively, the computer device can directly input the distance field, the heart segmentation result, the first blood vessel segmentation result, and the second blood vessel segmentation result into the second segmentation model, and the second segmentation model first maps and merges each tissue in the heart segmentation result with the corresponding tissue in the first blood vessel segmentation result and the second blood vessel segmentation result to obtain a merged segmentation result, and the second segmentation model then processes the distance field and the merged segmentation result to obtain a target blood vessel mask. The merged segmentation result obtained after the first blood vessel segmentation result and the second blood vessel segmentation result are mapped and merged can be a segmentation result corresponding to all blood vessel regions in the heart segmentation result; the distance field input into the second segmentation model can be a distance field of a heart chamber and / or a distance field of an aortic arch.

[0096] It should be noted that the computer device can perform network model training on the initial second segmentation model through the distance field of the heart chamber and the distance field training set of the aorta and aortic arch, the heart segmentation result and the training set of all blood vessel segmentation results to obtain a pre-trained second segmentation model. Specifically, the computer device can input the distance field of the heart chamber and the distance field of the aorta and aortic arch in the training set, the heart segmentation result and the blood vessel segmentation result of the entire blood vessel area in the training set into the initial second segmentation model. The initial second segmentation model first trains the heart segmentation result and the blood vessel segmentation result of the entire blood vessel area in the training set. The segmentation result is mapped and merged with the vascular segmentation result of the entire vascular region to obtain a merged segmentation result, and then each point of the vascular region in the merged segmentation result is voted to obtain a multi-vascular prediction mask, and the prediction error value between the multi-vascular prediction mask and the standard multi-vascular mask is calculated by the loss function, and the initial network parameters in the initial second segmentation model are updated according to the prediction error value, and the above training steps are continuously iterated until the prediction error value meets the preset error threshold or the number of iterations reaches the preset iteration number threshold, and the pre-trained second segmentation model is obtained; wherein, when the second segmentation model is used, the second segmentation model finally outputs the target vascular mask. All vascular segmentation results in this embodiment correspond to the vascular region corresponding to the combination of the first vascular segmentation result and the second vascular segmentation result.

[0097] It should be noted that the second segmentation model may include five input channels and 17 output channels, the five input channels are respectively a heart segmentation result input channel, a first blood vessel segmentation result input channel, a second blood vessel segmentation result input channel, a distance field input channel of a heart chamber, and a distance field input channel of an aortic aortic arch, wherein the 17 output channels may respectively output the above 15 masks, a bypass mask, and a background mask corresponding to a non-vascular region in the heart segmentation result. In this embodiment, the structures of the first segmentation model and the second segmentation model may be the same, as long as the sampling ratio of the bypass data is increased when training the second segmentation model, so that the second segmentation model has better segmentation performance for the bypass data, but the network parameters of the first segmentation model and the second segmentation model are different.

[0098] The above bypass reconstruction method can continue to obtain the second blood vessel segmentation result by obtaining the remaining blood vessel segmentation results within the blood vessel range when it is determined that the first blood vessel segmentation result contains bypass data, and combine the first blood vessel segmentation result with the second blood vessel segmentation result to obtain the blood vessel segmentation result corresponding to the entire blood vessel area, and further process the heart segmentation result, the distance field and the blood vessel segmentation result corresponding to the entire blood vessel area through the neural network model to obtain the target blood vessel mask of the entire blood vessel area, thereby improving the integrity and accuracy of the obtained blood vessel mask, and the neural network model processing can shorten the time for determining the blood vessel mask, further improving the efficiency of bypass reconstruction.

[0099] As one of the embodiments, Figure 6 As shown, the step of determining whether the first blood vessel segmentation result includes bridging data according to the initial blood vessel mask in the above S1131 can be implemented by the following steps:

[0100] S1131a. Determine a vascular connectivity domain according to the first blood vessel segmentation result, and obtain relevant information of the vascular connectivity domain; the relevant information of the vascular connectivity domain includes an initial spacing distance of the vascular connectivity domain in the distance field of the heart chamber or a size of the vascular connectivity domain.

[0101] Specifically, the computer device may select any point among all the blood vessel segmentation points corresponding to all the first blood vessel segmentation results in the merged segmentation result as a reference point, obtain a preset number of blood vessel segmentation points around the reference point, determine the reference point and all the blood vessel segmentation points that are blood vessel masks among the preset number of blood vessel segmentation points around the reference point according to the initial blood vessel mask, and use the reference point and all the blood vessel segmentation points that are blood vessel masks among the preset number of blood vessel segmentation points as a blood vessel connection domain. At the same time, the computer device may traverse all the blood vessel segmentation points corresponding to all the first blood vessel segmentation results in the merged segmentation result, and then determine the blood vessel connection domain corresponding to each blood vessel segmentation point. If the first blood vessel segmentation result corresponds to a three-dimensional image, the preset number of blood vessel segmentation points plus 1 blood vessel segmentation point (i.e., the reference point and the preset number of blood vessel segmentation points around the reference point) may be selected from all the blood vessel segmentation points corresponding to the first blood vessel segmentation result to determine the blood vessel connection domain. In which, at least one other blood vessel segmentation point can be found to be connected to the blood vessel segmentation point in the blood vessel connection domain to which any blood vessel segmentation point belongs, that is, at least one other blood vessel segmentation point can be found to be connected to the current blood vessel segmentation point in the same neighborhood (the preset number of surrounding blood vessel segmentation points, i.e., the preset number of neighborhoods). The above-mentioned preset number may be any value, as long as it is ensured that any blood vessel segmentation point can find at least one other blood vessel segmentation point to be connected with it in the blood vessel connection domain to which it belongs.

[0102] The above-mentioned blood vessel segmentation result may be blood vessel segmentation data. The blood vessel segmentation data may be in the form of point coordinates, which are displayed in a coordinate system, and each blood vessel segmentation result has a corresponding blood vessel segmentation point.

[0103] It is understandable that the computer device can determine the distance corresponding to each vascular connection domain in the distance field of the heart chamber, that is, the initial spacing distance, and at the same time, can also obtain the size of the vascular connection domain. The size of the above-mentioned vascular connection domain can be equal to the total number of vascular segmentation points included in the vascular connection domain. In this embodiment, the number of initial spacing distances corresponding to each vascular connection domain can be equal to the number of distances included in the distance field of the heart chamber.

[0104] S1131b: Determine whether the first blood vessel segmentation result includes bridging data according to the initial blood vessel mask and the related information of the blood vessel connected domain.

[0105] Specifically, the computer device can perform comparison processing, calculation processing, screening processing and / or analysis processing based on the relevant information of the initial blood vessel mask and the blood vessel connected domain to determine whether all the first blood vessel segmentation results in each blood vessel connected domain contain bridging data.

[0106] The above-mentioned bypass reconstruction method can determine the vascular connectivity domain based on the first vascular segmentation result, and obtain relevant information of the vascular connectivity domain. According to the initial vascular mask and the relevant information of the vascular connectivity domain, it is determined whether the first vascular segmentation result contains bypass data. When it is determined that the first vascular segmentation result in the vascular connectivity domain contains bypass data, the target vascular mask corresponding to the entire blood vessel in the heart segmentation result is obtained, thereby improving the accuracy and completeness of the obtained vascular mask.

[0107] As one of the embodiments, the initial vessel mask includes a bridging mask; Figure 7 As shown, the step of determining whether the first blood vessel segmentation result includes bridging data according to the initial blood vessel mask and the related information of the blood vessel connected domain in the above S1131b may specifically include:

[0108] S1131c: If the initial interval distance is greater than a first preset distance threshold or the size of the vascular connected domain is greater than a first preset number threshold, obtain a first total number of first blood vessel segmentation results corresponding to the bridging masks contained in other vascular connected domains.

[0109] Specifically, for each vascular connection domain, the computer device can determine whether the initial interval distances corresponding to the current vascular connection domain in the distance field of the heart chamber are all greater than the first preset distance threshold. If the judgment result is yes, the current vascular connection domain is not considered. At this time, other vascular connection domains outside the current vascular connection domain can be obtained, and the first total number of vascular segmentation results corresponding to all bridging masks contained in each other vascular connection domain is obtained; each other vascular connection domain has a corresponding first total number, and the first total number can be greater than or equal to 1.

[0110] Meanwhile, the first total number can also be determined in another way. Specifically, the computer device can first calculate the size of each vascular connected domain, determine whether the size of the current vascular connected domain is greater than the first preset number threshold, and if the determination result is yes, the current vascular connected domain is not considered, and the first total number of vascular segmentation results corresponding to all bridging masks contained in other vascular connected domains other than the current vascular connected domain is obtained. The size of the vascular connected domain can be understood as the total number of vascular segmentation points contained in the vascular connected domain.

[0111] In addition, for each vascular connection domain, the computer device can also determine whether the initial interval distances corresponding to the current vascular connection domain in the distance field of the heart chamber are greater than or equal to a first preset distance threshold. If the judgment result is yes, the current vascular connection domain is not considered. At this time, other vascular connection domains outside the current vascular connection domain can be obtained, and the first total number of vascular segmentation results corresponding to all bypass masks contained in each other vascular connection domain is obtained; or, the size of each vascular connection domain can be calculated first, and it can be determined whether the size of the current vascular connection domain is greater than or equal to the first preset number threshold. If the judgment result is yes, the current vascular connection domain is not considered, and the first total number of vascular segmentation results corresponding to all bypass masks contained in other vascular connection domains outside the current vascular connection domain is obtained.

[0112] It is understandable that the specific values ​​of the first preset distance threshold and the first preset quantity threshold are not limited, and can be customized by the user according to actual needs, or can be obtained by cross-validation and taking the average value of each verification result. Among them, for different types of preset thresholds, cross-validation can be understood as dividing the data set corresponding to the preset threshold into multiple parts, taking one of them as the verification set, and the remaining data set as the training set. Each time the corresponding threshold is adjusted on the training set to achieve the optimal value, and the corresponding threshold when the maximum accuracy is obtained in the verification is used as the optimal result of each verification, that is, the preset threshold. For example, when determining the first preset distance threshold, the data set can be a set corresponding to multiple initial interval distances.

[0113] S1131d. If the first total number is greater than a second preset number threshold, determine the first blood vessel segmentation result corresponding to the bridging mask contained in other blood vessel connected domains as bridging data, and determine that the first blood vessel segmentation result in other blood vessel connected domains contains bridging data.

[0114] Further, the computer device may continue to determine whether the first total number of first blood vessel segmentation results corresponding to the bypass masks contained in other blood vessel connected domains is greater than the second preset number threshold. If the determination result is yes, the first blood vessel segmentation results corresponding to all bypass masks contained in other blood vessel connected domains may be determined as bypass data. In this case, it is directly determined that the first blood vessel segmentation results in other blood vessel connected domains contain bypass data. In addition, the computer device may also determine whether the first total number of first blood vessel segmentation results corresponding to the bypass masks contained in other blood vessel connected domains is equal to the second preset number threshold. If the determination result is yes, the first blood vessel segmentation results corresponding to all bypass masks contained in other blood vessel connected domains may be determined as bypass data. In this case, it is directly determined that the first blood vessel segmentation results in other blood vessel connected domains contain bypass data. Optionally, the second preset number threshold and the first preset number threshold may be equal, and the specific determination method may also be the same. In this embodiment, for each blood vessel connected domain, the steps in S1131b and S1131d above are performed, that is, it is determined that the first blood vessel segmentation result in each blood vessel connected domain contains bypass data.

[0115] The above-mentioned bypass reconstruction method can determine whether the first blood vessel segmentation result contains bypass data, and then when it is determined that the first blood vessel segmentation result in the blood vessel connectivity domain contains bypass data, a target blood vessel mask corresponding to the entire blood vessel in the heart segmentation result can be obtained, thereby improving the accuracy and completeness of the acquired blood vessel mask.

[0116] As one of the embodiments, the step of obtaining the bypass reconstruction information according to the target blood vessel mask in the above S1200 may include: performing post-processing on the target blood vessel mask and the blood vessel segmentation result to obtain the bypass starting point, bypass path trajectory and anastomosis between the bypass and the normal blood vessel in the bypass reconstruction information.

[0117] Specifically, the post-processing may be at least one of comparison processing, calculation processing, screening processing, analysis processing, etc. That is, the computer device may perform transformation processing, comparison processing, calculation processing, screening processing, and / or analysis processing on the target blood vessel mask, the first blood vessel segmentation result, and the second blood vessel segmentation result to obtain the bypass reconstruction information, or may merge the first blood vessel segmentation result and the second blood vessel segmentation result based on the target blood vessel mask, and then determine the bypass reconstruction information according to the merged result.

[0118] In order to reduce the amount of calculation, the bypass reconstruction can be achieved by only determining the bypass starting point, the bypass path, and the anastomosis between the bypass and the normal blood vessel in the bypass reconstruction information. Therefore, in this embodiment, the bypass reconstruction information may include the bypass starting point, the bypass path, and the anastomosis between the bypass and the normal blood vessel. The number of the bypass starting point, the bypass path, and the anastomosis between the bypass and the normal blood vessel may be greater than 1.

[0119] For example, Figure 7a The diagram is a diagram of multiple frames of cardiac images of a diagnosis and treatment object, cardiac segmentation results corresponding to the cardiac images, a first blood vessel segmentation result, an initial blood vessel mask corresponding to the first blood vessel segmentation result, the first blood vessel segmentation result and the second blood vessel segmentation result (i.e., the blood vessel segmentation result of the entire blood vessel region on the heart), a target blood vessel mask corresponding to the first blood vessel segmentation result and the second blood vessel segmentation result, and a bypass reconstruction result. Different blood vessel segmentation points correspond to different blood vessel masks, and therefore, different blood vessel segmentation points of different blood vessel masks have different label values. In the images corresponding to the initial blood vessel mask and the target blood vessel mask, different blood vessel segmentation points of different blood vessel masks will display different label values.

[0120] The above-mentioned bypass reconstruction method can obtain bypass reconstruction information through post-processing of the complete blood vessel mask and the complete blood vessel segmentation result, thereby improving the completeness and accuracy of the bypass reconstruction information, and further improving the accuracy of the bypass reconstruction result.

[0121] The second bridge reconstruction method

[0122] The bridge reconstruction method can be described in detail by the following embodiments:

[0123] like Figure 8 FIG. 1 is a flow chart of a bridge reconstruction method provided by an embodiment, wherein the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0124] S2000. Obtain heart segmentation results and blood vessel segmentation results.

[0125] Specifically, the computer device can input multiple frames of cardiac images of the diagnosis and treatment object into a pre-trained cardiac segmentation model to obtain a cardiac segmentation result. Further, the vascular range is drawn through the cardiac segmentation result, and the data corresponding to the vascular range is segmented to obtain a vascular segmentation result. Optionally, the vascular segmentation result can be a segmentation result corresponding to the entire vascular area in the cardiac segmentation result. The above-mentioned cardiac image can be a three-dimensional CT enhanced image in dicom format. The above-mentioned cardiac segmentation result can be a cardiac segmentation image including the various chambers of the heart, the aorta, and the aortic arch, and the various chambers of the heart, the aorta, and the aortic arch in the cardiac segmentation image have corresponding labels to distinguish different tissues in the cardiac segmentation image. The various chambers of the heart can be the left atrium, right atrium, left ventricle, and right ventricle of the heart. In addition, the above-mentioned cardiac segmentation result can also be represented by cardiac segmentation data including the various chambers of the heart, the aorta, and the aortic arch.

[0126] It can be understood that the above-mentioned pre-trained heart segmentation model can be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model; wherein, the computer device can perform network model training on the initial heart segmentation model through a heart image training set to obtain a pre-trained heart segmentation model.

[0127] At the same time, the heart segmentation result can be taken as a whole to determine the blood vessel range. Among them, the computer device can extend the heart area range in the heart segmentation result outward by a certain preset range to obtain the blood vessel range, and further, input the blood vessel range into a pre-trained blood vessel segmentation model to obtain the blood vessel segmentation result. The above preset range can be customized according to actual conditions, as long as the blood vessel range is smaller than the range where the heart image is located. If the heart segmentation result is represented by a heart segmentation image, the blood vessel range can be an image representation obtained by extending the heart segmentation image outward by a certain preset range; if the heart segmentation result is represented by heart segmentation data, the blood vessel range can be a heart segmentation data representation corresponding to the image obtained by extending the heart segmentation image outward by a certain preset range. The above blood vessel segmentation result can be a binary blood vessel segmentation result.

[0128] The pre-trained blood vessel segmentation model may be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model. If the pre-trained blood vessel segmentation model and the pre-trained heart segmentation model have the same structure, the network parameters of the two may be different after network model training. The computer device may perform network model training on the initial blood vessel segmentation model using a blood vessel range training set to obtain a pre-trained blood vessel segmentation model.

[0129] In addition, both the heart segmentation model and the blood vessel segmentation model can complete network model training before executing S2000 in this embodiment.

[0130] S2100 , obtaining a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model.

[0131] Specifically, the computer device may process the heart segmentation result and the blood vessel segmentation result to obtain a processing result, and input the processing result into the segmentation model to obtain a multi-vessel mask corresponding to each point in the blood vessel segmentation result, i.e., a target blood vessel mask, or directly input the heart segmentation result and the blood vessel segmentation result into the segmentation model to obtain a multi-vessel mask corresponding to each point in the blood vessel segmentation result, i.e., a target blood vessel mask. In this embodiment, the heart segmentation result and the blood vessel segmentation result may be understood as images or data.

[0132] It is understandable that the segmentation model may be a pre-trained neural network model, and the segmentation model may be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model. If the pre-trained segmentation model has the same structure as the pre-trained blood vessel segmentation model and the pre-trained heart segmentation model, the network parameters of the three may be different after the network model training. In addition, the segmentation model may complete the network model training before executing S2100 in this embodiment.

[0133] It should be noted that the computer device can input the data in the training set corresponding to the heart segmentation result and the vascular segmentation result of the entire blood vessel in the heart segmentation result into the initial segmentation model to obtain a vascular prediction mask, calculate the prediction error value between the vascular prediction mask and the standard vascular mask through the loss function, and update the initial network parameters in the initial segmentation model according to the prediction error value, and continuously iterate the above training steps until the prediction error value meets the preset error threshold or the number of iterations reaches the preset iteration number threshold, so as to obtain a pre-trained segmentation model. The above-mentioned standard vascular mask can be an idealized vascular mask corresponding to the entire blood vessel. In this embodiment, the vascular prediction mask output by the segmentation model can be a vascular mask corresponding to the entire blood vessel.

[0134] S2200: Obtain bypass reconstruction information based on the target blood vessel mask and the blood vessel segmentation result, where the bypass reconstruction information includes a bypass starting point and an anastomosis between the bypass and a normal blood vessel.

[0135] Specifically, the computer device can transform, compare, calculate, filter and / or analyze the target vessel mask and the vessel segmentation result to obtain the bypass reconstruction information, or filter the target bypass data from the target vessel mask according to preset conditions, and analyze the target bypass data and the vessel segmentation result to determine the bypass reconstruction information. The above-mentioned target vessel mask can be understood as a multi-vessel label mask. The above-mentioned bypass reconstruction information can include the bypass path, the range of the bypass path, the bypass start point and the bypass end point in the bypass path, the size of the bypass path, and at least one of the various passing points in the bypass path. However, in this embodiment, the bypass reconstruction information can include the bypass start point and the anastomosis between the bypass and the normal blood vessel. The anastomosis between the bypass and the normal blood vessel can be understood as the connection point between the bypass and the normal blood vessel.

[0136] S2300: Process the bridging reconstruction information to obtain a bridging reconstruction result.

[0137] Specifically, the computer device can process the bypass reconstruction information to obtain a three-dimensional heart bypass model diagram, that is, the bypass reconstruction result. In addition, the computer device can first process the normal blood vessels in sections to obtain the centerline information of all blood vessels, and then process the centerline information of all blood vessels and the bypass reconstruction information to obtain the bypass reconstruction result. Furthermore, the computer device can output the bypass reconstruction result and display it to the doctor for viewing, so that the doctor can make regular return visits to the diagnosis and treatment object according to the bypass reconstruction result.

[0138] In the above-mentioned bypass reconstruction method, the computer device can obtain the heart segmentation result and the blood vessel segmentation result, obtain the target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model, obtain the bypass reconstruction information according to the target blood vessel mask, and obtain the bypass reconstruction result through processing through the bypass reconstruction information; this method obtains the bypass reconstruction information through a neural network model, and determines the bypass reconstruction result through reconstruction of the bypass reconstruction information, which can avoid manual reconstruction of the bypass model, save human resources and bypass reconstruction time, reduce the bypass reconstruction cost, and further improve the bypass reconstruction efficiency; at the same time, this method realizes bypass reconstruction through a neural network model, which can avoid reconstruction errors caused by manual participation in the reconstruction process, thereby improving the accuracy of bypass reconstruction.

[0139] As one of the embodiments, Fig. 9 As shown, the step of obtaining the bridging reconstruction information based on the target blood vessel mask and the blood vessel segmentation result in the above S2200 can be implemented by the following steps:

[0140] S2210. Obtain the blood vessel centerline through the blood vessel segmentation result.

[0141] Specifically, the computer device can perform skeletonization processing on the blood vessel segmentation result to obtain at least one blood vessel centerline. All blood vessel segmentation results can be represented by a blood vessel segmentation image, and each blood vessel segmentation result can be understood as blood vessel segmentation data. The above skeletonization processing can be understood as reducing the binary object to a representation of 1 pixel width.

[0142] Alternatively, the computer device may also determine the starting blood vessel layer of the starting blood vessel segment and the ending blood vessel layer of the ending blood vessel segment through the blood vessel segmentation image, and use the center of the starting blood vessel layer of the starting blood vessel segment as the starting point of the blood vessel centerline, and use the center of the ending blood vessel layer of the ending blood vessel segment as the ending point of the blood vessel centerline, and then use the maximum inscribed sphere method to extract the blood vessel centerline from the blood vessel segmentation image based on the starting point and the ending point of the blood vessel centerline. Of course, other methods may also be used to obtain the blood vessel centerline through the blood vessel segmentation result, which is not limited thereto.

[0143] S2220. Determine candidate points for bridging based on all blood vessel points on the blood vessel centerline.

[0144] Specifically, the computer device may obtain all the blood vessel points on the center line of each blood vessel, and use some of the blood vessel points as candidate bridge points. The candidate bridge points may be some of the beginning and end points and / or the end points on the center line of each blood vessel.

[0145] Among them, Fig.10 As shown, the step of determining the candidate bridging points according to all the blood vessel points on the blood vessel centerline in the above S2220 may specifically include:

[0146] S2221. Obtain a blood vessel point set corresponding to all blood vessel points on the blood vessel centerline, and obtain blood vessel endpoints in the blood vessel point set.

[0147] It is understandable that the computer device can obtain all the vascular points on the vascular centerline and store these vascular points in a vascular point set. When storing, the vascular points can be stored together with their respective coordinates. At the same time, the two endpoints on each vascular centerline can be screened out from all the vascular points, and the coordinates of the two endpoints on each vascular centerline can be obtained. Further, each endpoint of each vascular centerline can be traversed in turn, and each traversed endpoint is used as a vascular endpoint.

[0148] Optionally, each blood vessel centerline may correspond to a blood vessel point set, and the order of different blood vessel points stored in the blood vessel point set may be equal to the position order of all blood vessel points from any end point of the blood vessel to the other end point of the blood vessel on the corresponding blood vessel centerline.

[0149] S2222: Determine a preset number of target blood vessel points in the blood vessel point set that include blood vessel endpoints.

[0150] It should be noted that the computer device can determine, based on all the blood vessel points in the blood vessel point set, a preset number of target blood vessel points before or after the blood vessel endpoint of the corresponding blood vessel centerline in each blood vessel point set. The preset number can be less than or equal to the total number of all blood vessel points on the blood vessel centerline where the current blood vessel endpoint is located. All blood vessel points on each blood vessel centerline can include two blood vessel endpoints on the blood vessel centerline.

[0151] S2223. Determine candidate points for bridging based on the target blood vessel point.

[0152] Specifically, the computer device can determine whether a vessel endpoint among a preset number of target vessel points is a bridging candidate point based on a target vessel mask of a preset number of target vessel points on each vessel centerline. There can be at least one bridging candidate point among all vessel endpoints on all vessel centerlines. The bridging candidate point can be represented by the vessel point coordinates.

[0153] This embodiment can screen out candidate bridging points from all the vascular points on the vascular centerline, and further determine the bridging starting point based on the candidate bridging points. This process can narrow the candidate range of the bridging starting point, thereby reducing the amount of calculation to determine the bridging starting point and improving the speed of determining the bridging starting point.

[0154] S2230: Determine a bridge starting point based on the bridge candidate points.

[0155] Specifically, the computer device may directly determine the bridging candidate point as the bridging starting point, or determine some of all the bridging candidate points as the bridging starting point. In this embodiment, for each blood vessel centerline, the above steps S2221-S2224 are performed.

[0156] The above-mentioned bridge reconstruction method can determine the bridge candidate points in the blood vessel segmentation results, and then determine the bridge starting point through the bridge candidate points. This method avoids manual participation, can save human resources and time for determining the bridge starting point, and reduce the cost of determining the bridge starting point. At the same time, there is no need for manual participation in determining the bridge starting point, which can improve the accuracy of the determined bridge starting point.

[0157] As one of the embodiments, the step of determining the bypass candidate point according to the target blood vessel point in S2223 may include at least one of the following situations: if the second total number of bypass blood vessel points among all target blood vessel points is greater than the third preset number threshold, the blood vessel endpoints among all target blood vessel points are determined as bypass candidate points; if the distance between each target blood vessel point and the highest point in the heart chamber is greater than the second preset distance threshold, the blood vessel endpoints among all target blood vessel points are determined as bypass candidate points. If the minimum distance between the blood vessel endpoints among all target blood vessel points and the aortic arch is less than the third preset distance threshold, the blood vessel endpoint is determined as a bypass candidate point.

[0158] Specifically, the computer device can obtain the total number of target blood vessel points belonging to the bypass mask from the preset number of target blood vessel points (i.e., the second total number), and at the same time, calculate the distance between each target blood vessel point in the preset number of target blood vessel points and the highest point in the heart chamber, and then determine whether the second total number is greater than a third preset number threshold, and / or whether the distance between each target blood vessel point in the preset number of target blood vessel points and the highest point in the heart chamber is greater than the second preset distance threshold. If the judgment result is yes, the blood vessel endpoints in the preset number of target blood vessel points can be determined as bypass candidate points; otherwise, if it is determined that the second total number is less than or equal to the third preset number threshold, and / or at least one of the distances between each target blood vessel point in the preset number of target blood vessel points and the highest point in the heart chamber is less than or equal to the second preset distance threshold, the current preset number of target blood vessel points can be filtered out, that is, in this case, there are no bypass candidate points among the current preset number of target blood vessel points.

[0159] Of course, the computer device can also determine whether the second total number is equal to the third preset number threshold, and / or whether the distance between each target blood vessel point in the preset number of target blood vessel points and the highest point in the heart chamber is greater than or equal to the second preset distance threshold. If the judgment result is yes, the blood vessel endpoints in the preset number of target blood vessel points can be determined as candidate bridge points; otherwise, if it is determined that the second total number is less than the third preset number threshold, and / or at least one of the distances between each target blood vessel point in the preset number of target blood vessel points and the highest point in the heart chamber is less than the second preset distance threshold, the current preset number of target blood vessel points can be filtered out, that is, in this case, there are no candidate bridge points in the current preset number of target blood vessel points. Optionally, the distance between each target blood vessel point in the preset number of target blood vessel points and the highest point in the heart chamber area can be understood as the distance above the heart, wherein the highest point in the heart chamber area can be the point corresponding to the maximum coordinate of the heart chamber in the coronary position.

[0160] At the same time, the computer device can also calculate the minimum distance between the blood vessel endpoint and the aortic arch among a preset number of target blood vessel points, and whether the minimum distance between the blood vessel endpoint and the aortic arch is less than or equal to a third preset distance threshold. If the judgment result is yes, the blood vessel endpoint among the preset number of target blood vessel points can be determined as a candidate point for bypass surgery; otherwise, if the minimum distance between the blood vessel endpoint and the aortic arch is less than or equal to the third preset distance threshold, the current preset number of target blood vessel points can be filtered out, that is, in this case, there are no candidate points for bypass surgery among these current preset number of target blood vessel points.

[0161] It is understandable that the third preset quantity threshold, the second preset distance threshold and the third preset distance threshold may be equal or unequal, and may be determined according to actual conditions. In this embodiment, the third preset quantity threshold may be greater than 1 / 2 of the preset quantity, the preset quantity may be equal to 50, the second preset distance threshold may be equal to 0.5 cm, and the third preset distance threshold may be equal to 1.5 cm.

[0162] The above-mentioned bridge reconstruction method can determine the candidate bridge points in the blood vessel segmentation results, thereby narrowing the candidate range of the bridge starting point, reducing the amount of calculation for determining the bridge starting point, and increasing the speed of determining the bridge starting point, so that the bridge starting point can be quickly obtained in a short time.

[0163] As one of the embodiments, Fig.11 As shown, the above S2230 determines the bridge starting point based on the bridge candidate point, which can be achieved by the following steps:

[0164] S2231. Determine the target blood vessel connectivity domain based on the blood vessel segmentation result.

[0165] Specifically, the computer device can select any point among all the blood vessel segmentation points corresponding to the blood vessel segmentation result of the entire blood vessel region as a reference point, obtain a preset number of blood vessel segmentation points around the reference point, and determine the reference point and all the blood vessel segmentation points that are blood vessel masks among the preset number of blood vessel segmentation points around the reference point according to the target blood vessel mask, and use the reference point and all the blood vessel segmentation points that are blood vessel masks among the preset number of blood vessel segmentation points as a target blood vessel connected domain. At the same time, the computer device can traverse each blood vessel segmentation point corresponding to the blood vessel segmentation result, and then determine the target blood vessel connected domain corresponding to each blood vessel segmentation point. If the blood vessel segmentation result corresponds to a three-dimensional image, the preset number of blood vessel segmentation points plus 1 blood vessel segmentation point (i.e., the reference point and the preset number of blood vessel segmentation points around the reference point) can be selected from all the blood vessel segmentation points corresponding to the blood vessel segmentation result to determine the target blood vessel connected domain. Among them, at least one other blood vessel segmentation point can be found in the target blood vessel connected domain to which any blood vessel segmentation point belongs to be connected to the selected current blood vessel segmentation point, that is, at least one other blood vessel segmentation point can be found in the same neighborhood (the preset number of surrounding blood vessel segmentation points, that is, the preset number of neighborhoods) to be connected to the current blood vessel segmentation point. In this embodiment, the preset number may be any value, as long as it is ensured that any blood vessel segmentation point can find at least one other blood vessel segmentation point to be connected with it within the target blood vessel connection domain.

[0166] It should be noted that each target blood vessel connected domain may be a collection of multiple blood vessel segmentation points.

[0167] S2232: Determine the target blood vessel connected domain to which the candidate bridging point belongs based on the candidate bridging point and the target blood vessel connected domain.

[0168] Specifically, for each bridging candidate point, the computer device may match the bridging candidate point with a vessel segmentation point in the target vessel connectivity domain, and determine the target vessel connectivity domain to which the successfully matched bridging candidate point belongs. Optionally, the target vessel connectivity domain to which the bridging candidate point belongs may be any one of all the target vessel connectivity domains.

[0169] S2233. Obtain the interval distance between any two bridging candidate points in the target blood vessel connectivity domain.

[0170] It is understandable that the computer device can use the Euclidean distance, Manhattan distance, cosine distance, Minkowski distance or Chebyshev distance calculation method to calculate the interval distance between all the bridging candidate points in the target vessel connection domain to which the bridging candidate point belongs. The target vessel connection domain to which the bridging candidate point belongs may include at least two bridging candidate points. If the target vessel connection domain to which the bridging candidate point belongs includes n (n>2) bridging candidate points, an interval distance can be determined for every two bridging candidate points among the n bridging candidate points, and n*(n-1) / 2 interval distances can be determined for the n bridging candidate points.

[0171] S2234. Determine the starting point of the bridge according to the interval distance.

[0172] It should be noted that the computer device can compare the interval distance with a preset distance threshold, and determine whether the bridge candidate point is the bridge starting point according to the comparison result. The comparison result can be that the interval distance is greater than, less than or equal to the preset distance threshold. In this embodiment, the preset distance threshold can be any value, which is not limited to this.

[0173] The above-mentioned bridge reconstruction method can determine the bridge starting point through the bridge candidate point, and then obtain the bridge reconstruction result through processing through the bridge starting point; this method avoids manual participation, can save human resources and time for determining the bridge starting point, and reduce the cost of determining the bridge starting point. At the same time, no manual participation is required to determine the bridge starting point, which can improve the accuracy of the determined bridge starting point and further improve the accuracy of the bridge reconstruction result; in addition, this method can determine the bridge starting point within a smaller range, thereby reducing the amount of calculation for determining the bridge starting point, improving the speed of determining the bridge starting point, and can quickly obtain the bridge starting point in a short time, thereby improving the efficiency of bridge reconstruction.

[0174] As one of the embodiments, Fig.12 As shown, the step of determining the starting point of the bridge according to the interval distance in the above S2234 may specifically include the following steps:

[0175] S2234a: If the interval distance is less than a fourth preset distance threshold, retain the bypass candidate point closest to the aorta among the bypass candidate points corresponding to the interval distance to obtain a first retained candidate point.

[0176] It should be noted that the computer device can determine whether the interval distance corresponding to each bypass candidate point is less than or equal to the fourth preset distance threshold. If the judgment result is yes, the bypass candidate point closest to the aorta among all the bypass candidate points corresponding to the interval distance is retained, and the retained bypass candidate point is used as the first retained candidate point. The interval distance corresponding to the above-mentioned bypass candidate point can be the interval distance between any two bypass candidate points in all the bypass candidate points in the target vascular connectivity domain to which the bypass candidate point belongs. The above-mentioned fourth preset distance threshold, the second preset distance threshold and the third preset distance threshold can be equal or unequal, and can all be determined according to actual conditions. In this embodiment, the above-mentioned fourth preset distance threshold can be equal to 0.5 cm.

[0177] S2234b. If the distance between the candidate bridging point within the target vascular connectivity domain and the outer surface of the heart chamber is greater than a fifth preset distance threshold, then, based on the target vascular mask, obtain a third total number of candidate bridging points belonging to the left anterior descending branch mask and / or the diagonal branch mask among all vascular segmentation points within the target vascular connectivity domain; the target vascular mask includes the left anterior descending branch mask and / or the diagonal branch mask.

[0178] Specifically, the computer device can determine whether the distance between all the bypass candidate points in each target vascular connection domain and the outer surface of the heart chamber is greater than the fifth preset distance threshold. If the judgment result is yes, these bypass candidate points can be regarded as internal mammary artery bypass data. At this time, the total number of bypass candidate points belonging to the left anterior descending branch mask and / or the diagonal branch mask among all the vascular segmentation points in the target vascular connection domain can be obtained according to the target vascular mask, that is, the third total number. In addition, the computer device can also determine whether the distance between all the bypass candidate points in each target vascular connection domain and the outer surface of the heart chamber is equal to the fifth preset distance threshold. If the judgment result is yes, these bypass candidate points can also be regarded as internal mammary artery bypass data. The fifth preset distance threshold, the fourth preset distance threshold, the second preset distance threshold and the third preset distance threshold can be equal or unequal, and can all be determined according to actual conditions. In this embodiment, the fifth preset distance threshold can be equal to 5cm.

[0179] In addition, the computer device can determine whether the shortest distance between all the bypass candidate points in the target blood vessel connection domain to which the bypass candidate point belongs and the outer surface of the heart chamber is greater than a preset distance. If so, the bypass candidate point can be discarded. The preset distance can be determined according to actual conditions. In this embodiment, the preset distance can be equal to 0.5 cm.

[0180] S2234c: If the third total number is greater than or equal to a fourth preset number threshold, determine the bridging candidate points belonging to the left anterior descending branch mask and the diagonal branch mask among all the vessel segmentation points in the target vessel connectivity domain as second retained candidate points.

[0181] Specifically, the computer device can determine whether the third total number is greater than or equal to a fourth preset number threshold. If the determination result is yes, the bridging candidate points belonging to the left anterior descending branch mask or the diagonal branch mask among all the vascular segmentation points in the target vascular connection domain can be retained, and these bridging candidate points can be determined as second retained candidate points. The fourth preset number threshold can be determined according to actual conditions.

[0182] S2234d. Determine the first reserved candidate point and the second reserved candidate point as bridge-building starting points.

[0183] Furthermore, the computer device may determine the acquired first candidate points for retention and the second candidate points for retention as bridge starting points. The number of bridge starting points may be equal to the total number of the first candidate points for retention and the second candidate points for retention.

[0184] The above-mentioned bridge reconstruction method can determine the starting point of the bridge, and then obtain the bridge reconstruction result through processing based on the bridge starting point; this method avoids manual participation, can save human resources and the time for determining the starting point of the bridge, and reduce the cost of determining the starting point of the bridge. At the same time, no manual participation is required to determine the starting point of the bridge, which can improve the accuracy of the determined starting point of the bridge, and further improve the accuracy of the bridge reconstruction result; in addition, this method can determine the starting point of the bridge within a smaller range, thereby reducing the amount of calculation for determining the starting point of the bridge, improving the speed of determining the starting point of the bridge, and can quickly obtain the starting point of the bridge in a short time, thereby improving the efficiency of bridge reconstruction.

[0185] As one of the embodiments, Fig.13 As shown, the step of obtaining the bridging reconstruction information based on the target blood vessel mask and the blood vessel segmentation result in the above S2200 can be implemented by the following steps:

[0186] S2240: Determine corresponding blood vessel segmentation points according to the blood vessel segmentation result.

[0187] Specifically, the above-mentioned blood vessel segmentation result may be blood vessel segmentation data. The blood vessel segmentation data may be in the form of point coordinates, which are displayed in a coordinate system, and each blood vessel segmentation result has a corresponding blood vessel segmentation point.

[0188] S2250, trace back each blood vessel segmentation point in sequence from the end point of the bypass to the starting point of the bypass along the bypass path, and determine the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point.

[0189] Specifically, the computer device can trace back each blood vessel segmentation point in sequence from the bypass end point of each bypass path to the bypass starting point along each bypass path, and according to the target blood vessel mask of the traced blood vessel segmentation point, any traced blood vessel segmentation point can be determined as the anastomosis between the bypass and the normal blood vessel. The number of determined anastomosis between the bypass and the normal blood vessel can be greater than or equal to 1. In this embodiment, the above-mentioned bypass path can be predetermined, and the bypass end point can be the bypass end point in the predetermined bypass path.

[0190] Among them, the step of determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point in the above S2250 may include: if the end point of the bypass is determined as the blood vessel mask point according to the target blood vessel mask of the blood vessel segmentation point, and the current blood vessel segmentation point is the bypass mask point, backtracking is stopped, and the current blood vessel segmentation point is determined as the anastomosis between the bypass and the normal blood vessel.

[0191] It should be noted that if the backtracked bypass tail point is determined as a vascular mask point based on the target vascular mask of each vascular segmentation point, and the computer device stops backtracking when the current vascular segmentation point in the bypass path trajectory to which the bypass tail point belongs is a bypass mask point, then the computer device can determine the backtracked current vascular segmentation point as the anastomosis between the bypass and the normal blood vessel. In this case, the anastomosis between the bypass and the normal blood vessel can be the bypass starting point, or it can be any vascular segmentation point between the bypass tail point and the bypass starting point. The above-mentioned vascular mask point can be understood as the vascular segmentation point where the target vascular mask is the vascular mask; the above-mentioned bypass mask point can be understood as the vascular segmentation point where the target vascular mask is the bypass mask.

[0192] Among them, the step of determining the anastomosis between the bypass and the normal blood vessel based on the target blood vessel mask of the blood vessel segmentation point in the above S2250 may also include: if the end point of the bypass is a bypass mask point, then backtracking is stopped when the current blood vessel segmentation point is a blood vessel mask point, and the current blood vessel segmentation point is determined as the anastomosis between the bypass and the normal blood vessel.

[0193] It can be understood that if the backtracked bypass tail point is determined to be a bypass mask point based on the target vessel mask of each vessel segmentation point, then the computer device can continue to backtrack forward and stop backtracking when the current vessel segmentation point in the bypass path trajectory to which the bypass tail point belongs is a vessel mask point. At this time, the current vessel segmentation point can be determined as the anastomosis between the bypass and the normal blood vessel.

[0194] At the same time, the step of determining the anastomosis between the bypass and the normal blood vessel based on the target blood vessel mask of the blood vessel segmentation point in the above S2250 may also include: if all blood vessel segmentation points between the current blood vessel segmentation point and the bypass tail point are bypass mask points, and the backtracking distance between the current blood vessel segmentation point and the bypass tail point is greater than a preset distance threshold, then the bypass tail point is determined as the anastomosis between the bypass and the normal blood vessel.

[0195] It is understandable that if all the vascular segmentation points (including the current vascular segmentation point and the bypass tail point) between the current vascular segmentation point and the bypass tail point traced back by the computer device are all bypass mask points, and the traced distance between the current vascular segmentation point and the bypass tail point is greater than or equal to the sixth preset distance threshold, at this time, the bypass tail point can be determined as the anastomosis between the bypass and the normal blood vessel. Among them, the current vascular segmentation point traced back and the corresponding bypass tail point are on the same bypass path trajectory. The above-mentioned sixth preset distance threshold can be determined according to actual conditions, and the specific value is not limited. In this embodiment, the above-mentioned sixth preset distance threshold can be equal to 3cm.

[0196] The above-mentioned bypass reconstruction method can determine the anastomosis between the bypass and the normal blood vessel, and then obtain the bypass reconstruction result by processing the anastomosis between the bypass and the normal blood vessel, the bypass starting point and the bypass end point. This method avoids manual reconstruction of the bypass model, can save human resources and bypass reconstruction time, reduce the cost of bypass reconstruction, and further improve the efficiency of bypass reconstruction.

[0197] The third bridge reconstruction method

[0198] The bridge reconstruction method can be described in detail by the following embodiments:

[0199] like Fig.14 FIG. 1 is a flow chart of a bridge reconstruction method provided by an embodiment, wherein the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0200] S3000. Obtain heart segmentation results and blood vessel segmentation results.

[0201] Specifically, the computer device can input multiple frames of cardiac images of the diagnosis and treatment object into a pre-trained cardiac segmentation model to obtain a cardiac segmentation result. Further, the vascular range is determined by the cardiac segmentation result, and the vascular range is segmented to obtain a vascular segmentation result. Optionally, the vascular segmentation result can be a segmentation result corresponding to the entire vascular area in the cardiac segmentation result. The above-mentioned cardiac image can be a three-dimensional CT enhanced image in dicom format. The above-mentioned cardiac segmentation result can be a cardiac segmentation image including each chamber of the heart, the aorta, and the aortic arch, and each chamber of the heart, the aorta, and the aortic arch in the cardiac segmentation image have corresponding labels to distinguish different tissues in the cardiac segmentation image. The various chambers of the heart can be the left atrium, right atrium, left ventricle and right ventricle of the heart. In addition, the above-mentioned cardiac segmentation result can also be represented by cardiac segmentation data including each chamber of the heart, the aorta, and the aortic arch.

[0202] It is understandable that the above-mentioned pre-trained heart segmentation model can be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model; wherein the computer device can perform network model training on the initial heart segmentation model through a heart image training set to obtain a pre-trained heart segmentation model. Specifically, the computer device can input the heart image in the heart image training set into the initial heart segmentation model to obtain a heart segmentation prediction result, calculate the prediction error value between the heart segmentation prediction result and the standard heart segmentation result through a loss function, and update the initial network parameters in the initial heart segmentation model according to the prediction error value, and continuously iterate the above training steps until the prediction error value meets the preset error threshold or the number of iterations reaches the preset iteration number threshold, thereby obtaining a pre-trained heart segmentation model. The above-mentioned heart image training set can be a collection of heart images of different diagnosis and treatment objects, and the above-mentioned standard heart segmentation result can be an idealized heart segmentation result.

[0203] At the same time, the heart segmentation result can be taken as a whole to determine the blood vessel range. Among them, the computer device can extend the heart segmentation result outward by a certain preset range to obtain the blood vessel range, and further, input the blood vessel range into a pre-trained blood vessel segmentation model to obtain the blood vessel segmentation result. The above preset range can be customized according to actual conditions, as long as the blood vessel range is smaller than the range where the heart image is located. If the heart segmentation result is represented by a heart segmentation image, the blood vessel range can be an image representation obtained by extending the heart segmentation image outward by a certain preset range; if the heart segmentation result is represented by heart segmentation data, the blood vessel range can be a heart segmentation data representation corresponding to the image obtained by extending the heart segmentation image outward by a certain preset range. The above blood vessel segmentation result can be a binary blood vessel segmentation result.

[0204] The pre-trained vascular segmentation model may be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model. If the pre-trained vascular segmentation model and the pre-trained heart segmentation model have the same structure, the network parameters of the two may be different after network model training. The computer device may perform network model training on the initial vascular segmentation model through the vascular range training set to obtain the pre-trained vascular segmentation model. Specifically, the computer device may input the vascular data corresponding to the vascular range in the vascular range training set into the initial vascular segmentation model to obtain the vascular segmentation prediction result, calculate the prediction error value between the vascular segmentation prediction result and the standard vascular segmentation result through the loss function, and update the initial network parameters in the initial vascular segmentation model according to the prediction error value, and continuously iterate the above training steps until the prediction error value meets the preset error threshold or the number of iterations reaches the preset number of iterations threshold, thereby obtaining the pre-trained vascular segmentation model. The vascular range training set may be a collection of vascular range data corresponding to different diagnosis and treatment objects, and the standard vascular segmentation result may be an idealized vascular segmentation result.

[0205] In addition, the above-mentioned heart segmentation model and blood vessel segmentation model can complete network model training before executing S3000 in this embodiment.

[0206] S3100 , obtaining a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model.

[0207] Specifically, the computer device may process the heart segmentation result and the blood vessel segmentation result to obtain a processing result, and input the processing result into the segmentation model to obtain a multi-vessel mask corresponding to each point in the blood vessel segmentation result, i.e., a target blood vessel mask, or directly input the heart segmentation result and the blood vessel segmentation result into the segmentation model to obtain a multi-vessel mask corresponding to each point in the blood vessel segmentation result, i.e., a target blood vessel mask. In this embodiment, the heart segmentation result and the blood vessel segmentation result may be understood as images or data.

[0208] It is understandable that the segmentation model can be a pre-trained neural network model, and the segmentation model can be composed of at least one of a convolutional neural network model, a recurrent neural network model, and an adversarial neural network model. If the pre-trained segmentation model has the same structure as the pre-trained blood vessel segmentation model and the pre-trained heart segmentation model, the network parameters of the three can be different after the network model training. In addition, the segmentation model can complete the network model training before executing S3100 in this embodiment.

[0209] S3200: Obtain bypass reconstruction information based on the target blood vessel mask and the blood vessel segmentation result. The bypass reconstruction information includes a bypass path and an anastomosis between the bypass and the normal blood vessel.

[0210] Specifically, the computer device may perform transformation processing, comparison processing, calculation processing, screening processing and / or analysis processing on the target blood vessel mask and the blood vessel segmentation result to obtain bypass reconstruction information, or screen the target bypass data from the target blood vessel mask according to preset conditions, and perform analysis processing on the target bypass data and the blood vessel segmentation result to determine the bypass reconstruction information. The above-mentioned target blood vessel mask may be understood as a multi-vessel label mask. In this embodiment, the above-mentioned target blood vessel mask may include at least one blood vessel mask and a bypass mask. The blood vessel mask may be any one of 15 masks, namely, right coronary artery (RCA) mask, right posterior descending coronary artery (R-PDA) mask, left ventricular right posterior branch (R-PLB) mask, left main coronary artery (LM) mask, left anterior descending artery (LAD) mask, diagonal branch (D) mask, circumflex branch (LCX) mask, obtuse marginal branch (OM) mask, left posterior descending artery (L-PDA) mask, left ventricular left posterior branch (L-PLB) mask, intermediate branch (RAMUS) mask, atrial branch (LACX) mask, sharp marginal branch (AM) mask, anterior septal branch (S) mask or sinoatrial node branch (LSN) mask. The above-mentioned bypass reconstruction information may include at least one of the bypass path, the range of the bypass path, the bypass starting point and the bypass end point in the bypass path, the size of the bypass path, and each passing point in the bypass path. However, in this embodiment, the bypass reconstruction information may include the bypass path and the anastomosis between the bypass and the normal blood vessel.

[0211] S3300. Process the bridging reconstruction information to obtain a bridging reconstruction result.

[0212] Specifically, the computer device can process the bypass starting point, the obtained bypass path, and the bypass reconstruction information of the anastomosis between the bypass and the normal blood vessels to obtain a three-dimensional heart bypass model diagram, that is, the bypass reconstruction result. In addition, the computer device can also first segment the normal blood vessels to obtain the centerline information of all blood vessels, and then process the centerline information of all blood vessels and the bypass reconstruction information to obtain the bypass reconstruction result. The above-mentioned bypass starting point can be known information, and can be the starting point in the bypass path. Furthermore, the computer device can output the bypass reconstruction result and display it to the doctor for viewing, so that the doctor can make regular return visits to the diagnosis and treatment object according to the bypass reconstruction result.

[0213] In the above-mentioned bypass reconstruction method, the computer device can obtain the heart segmentation result and the blood vessel segmentation result, obtain the target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model, obtain the bypass reconstruction information based on the target blood vessel mask and the blood vessel segmentation result, and obtain the bypass reconstruction result through processing through the bypass reconstruction information; the method is based on the neural network model to obtain the target blood vessel mask, and further obtains the bypass reconstruction information based on the target blood vessel mask, and then determines the bypass reconstruction result through reconstruction of the bypass reconstruction information, which can avoid manual reconstruction of the bypass model, save human resources and bypass reconstruction time, reduce the bypass reconstruction cost, and further improve the bypass reconstruction efficiency; at the same time, the method realizes bypass reconstruction based on the neural network model, which can avoid reconstruction errors caused by manual participation in the reconstruction process, thereby improving the accuracy of bypass reconstruction.

[0214] In some scenarios, whether the trajectory map corresponding to the constructed blood vessel segmentation points is complete and accurate will directly affect the completeness and accuracy of the determined bypass path trajectory. Therefore, in order to improve the completeness and accuracy of the determined bypass path trajectory, in this embodiment, Fig.15 As shown, the step of obtaining the bridging reconstruction information based on the target blood vessel mask and the blood vessel segmentation result in the above S3200 can be implemented by the following steps:

[0215] S3210: Determine a target blood vessel connected domain according to the blood vessel segmentation result and the target blood vessel mask.

[0216] Specifically, the computer device can select any point among all the blood vessel segmentation points corresponding to the blood vessel segmentation result of the entire blood vessel area as a reference point, and obtain a preset number of blood vessel segmentation points around the reference point, and determine the reference point and all the blood vessel segmentation points that are blood vessel masks among the preset number of blood vessel segmentation points around the reference point according to the target blood vessel mask, and use the reference point and all the blood vessel segmentation points that are blood vessel masks among the preset number of blood vessel segmentation points as a target blood vessel connected domain. At the same time, the computer device can traverse each blood vessel segmentation point corresponding to the blood vessel segmentation result, and then determine the target blood vessel connected domain corresponding to each blood vessel segmentation point.

[0217] It should be noted that, if the blood vessel segmentation result corresponds to a three-dimensional image, a preset number of all blood vessel segmentation points corresponding to the blood vessel segmentation result plus 1 blood vessel segmentation point (i.e., the reference point and the preset number of blood vessel segmentation points around the reference point) can be selected to determine the target blood vessel connected domain. Among them, at least one other blood vessel segmentation point can be found in the target blood vessel connected domain to which any blood vessel segmentation point belongs to be connected to the selected current blood vessel segmentation point, that is, at least one other blood vessel segmentation point can be found in the same neighborhood (the preset number of blood vessel segmentation points around, that is, the preset number of neighborhoods) to be connected to the current blood vessel segmentation point. The preset number can be any value, as long as it is ensured that any blood vessel segmentation point can find at least one other blood vessel segmentation point in the target blood vessel connected domain to which it belongs to be connected.

[0218] S3220 , construct a trajectory map corresponding to the blood vessel segmentation points through all corresponding blood vessel segmentation results in the target blood vessel connected domain.

[0219] Specifically, the computer device can perform skeletonization processing on the blood vessel segmentation result to obtain at least one blood vessel centerline. The above skeletonization processing can be understood as reducing the binary object to a representation of 1 pixel width. The starting point of the bridge corresponds to the starting point on the blood vessel centerline. The number of blood vessel centerlines can be equal to the number of trajectory maps. Each blood vessel centerline contains multiple blood vessel segmentation points, and each blood vessel segmentation point has its own target blood vessel connection domain.

[0220] Specifically, each blood vessel segmentation result can be understood as a point coordinate, that is, the blood vessel segmentation point coordinate. The computer device can first establish a dot graph through all the blood vessel segmentation results corresponding to each target blood vessel connected domain, and then, according to the position order of the bypass starting point in each target blood vessel connected domain and all blood vessel segmentation points on each blood vessel centerline, connect each adjacent blood vessel segmentation point in the dot graph in sequence starting from the bypass starting point in the dot graph to obtain at least one trajectory graph, that is, each trajectory graph includes multiple blood vessel segmentation points and edges between two adjacent blood vessel segmentation points, wherein each edge in the trajectory graph can also be a part of the bypass path trajectory. The number of trajectory graphs corresponding to each target blood vessel connected domain can be greater than or equal to 1, and can also be equal to the number of bypass starting points contained in the corresponding target blood vessel connected domain. Each edge in the trajectory graph can be understood as each path in the trajectory graph.

[0221] S3230, starting from the starting point of the bypass, traverse the adjacent blood vessel segmentation points backward in sequence along the trajectory diagram to determine the bypass path.

[0222] It should be noted that the above trajectory diagram can be in the form of a straight line or a broken line, and this is not limited. In this embodiment, the trajectory diagram can be in the form of a binary tree, so the blood vessel segmentation points in the trajectory diagram can be blood vessel segmentation points in the bypass path trajectory, or can be blood vessel segmentation points other than blood vessel segmentation points in the bypass path trajectory. However, in this embodiment, some blood vessel segmentation points in the trajectory diagram can be traversed, or all blood vessel segmentation points in the trajectory diagram can be traversed.

[0223] The above-mentioned bypass reconstruction method can determine the target vascular connectivity domain based on the vascular segmentation result and the target vascular mask, and construct a trajectory map corresponding to the vascular segmentation point through all the corresponding vascular segmentation results in the target vascular connectivity domain. Starting from the bypass starting point, the adjacent vascular segmentation points are traversed backward along the trajectory map to determine the bypass path. This process avoids manual construction of the trajectory map corresponding to the vascular segmentation points, and can achieve mapping through a machine learning algorithm, thereby improving the accuracy and completeness of the mapping results. It can further traverse a complete and accurate trajectory map, and can also improve the accuracy and completeness of the determined bypass path trajectory, and can provide accurate and complete bypass reconstruction information for determining the bypass reconstruction results. At the same time, this method avoids manual participation in determining the bypass path, which can save human resources and reduce the workload of medical staff.

[0224] As one of the embodiments, Fig.16 As shown, the step of starting from the bypass starting point and traversing the adjacent blood vessel segmentation points backward along the trajectory diagram in the above S3230 to determine the bypass path can be achieved by the following steps:

[0225] S3231. Starting from the starting point of the bypass, traverse the adjacent blood vessel segmentation points backwards in sequence along the trajectory diagram to determine the end point of the bypass.

[0226] Specifically, the computer device may start from the bridging starting point in each trajectory graph and sequentially traverse the adjacent blood vessel segmentation points backward to determine the bridging end point among all blood vessel segmentation points in the corresponding trajectory graph. A trajectory graph may include a bridging starting point and one or more bridging end points.

[0227] S3232: Determine the bypass path according to the bypass starting point, the bypass ending point, and the traversed blood vessel segmentation points.

[0228] It should be noted that the computer device can determine the bypass path according to the bypass starting point, the bypass end point, and all the blood vessel segmentation points that have been traversed between the bypass starting point and the bypass end point. A trajectory map may include one or more bypass paths. The above-mentioned bypass starting point may be a bypass point in a predetermined bypass path.

[0229] The above-mentioned bypass reconstruction method can start from the bypass starting point, traverse the adjacent blood vessel segmentation points backward along the trajectory diagram in sequence, determine the bypass tail point among all blood vessel segmentation points, and determine the bypass path based on the bypass starting point, the bypass tail point and the traversed blood vessel segmentation points. This method avoids manual participation in traversal to determine the bypass path, can save human resources, and reduce the workload of medical staff.

[0230] As one of the embodiments, Fig.17 As shown, the step of traversing the adjacent blood vessel segmentation points backwards in sequence along the trajectory diagram to determine the end point of the bypass in S3231 can be achieved by the following steps:

[0231] S3231a: If the current traversed blood vessel segmentation point is a first bifurcation point, determine at least one second bifurcation point adjacent to the first bifurcation point.

[0232] Specifically, if the current blood vessel segmentation point traversed by the computer device is a bifurcation point, that is, a first bifurcation point, then it can be determined that there are at least two adjacent blood vessel segmentation points after the current blood vessel segmentation point. Further, all adjacent blood vessel segmentation points after the first bifurcation point can be referred to as second intersection points, or an adjacent blood vessel segmentation point or a portion of adjacent blood vessel segmentation points after the first bifurcation point can be referred to as second intersection points. The number of first intersection points can be equal to 1, and the number of second intersection points can be greater than 1.

[0233] It should be noted that there are at least two vascular segmentation points in the neighborhood after the first bifurcation point, and then for all adjacent vascular segmentation points after the first bifurcation point, if there are only two vascular segmentation points in the neighborhood and they are vascular segmentation points on the edge of the trajectory graph, the vascular segmentation points that have not been traversed can be selected to continue the backward traversal. If there are no vascular segmentation points in the neighborhood, the current second bifurcation point can be determined as the end point of the bypass. If the total number of vascular segmentation points in the neighborhood is greater than 2, the current vascular segmentation point that has been traversed is determined as the second bifurcation point.

[0234] S3231b: If there is no bridging mask point among all the blood vessel segmentation points between the first bifurcation point and each second bifurcation point, determine whether there is a main branch blood vessel mask point among all the blood vessel segmentation points between the first bifurcation point and each second bifurcation point.

[0235] It is understandable that the computer device can determine whether there is a blood vessel segmentation point belonging to the bypass mask among all the blood vessel segmentation points between the first bifurcation point and any second bifurcation point. If it is determined that there is no bypass mask point among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point, it can continue to determine whether there is a blood vessel segmentation point belonging to the main branch blood vessel mask among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point. The current second intersection point can be any second intersection point among all the second intersection points. The number of bypass mask points among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point can be greater than or equal to 0, and the number of main branch blood vessel mask points among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point can be greater than or equal to 0.

[0236] It should be noted that the bypass mask point can be understood as a blood vessel segmentation point belonging to the bypass mask. The main branch vessel mask point can be understood as a blood vessel segmentation point belonging to the main branch vessel mask. There may be one or more other blood vessel segmentation points between the first intersection point and the second intersection point, or there may be no other blood vessel segmentation points between the first intersection point and the second intersection point.

[0237] S3231c: If yes, then starting from each second bifurcation point, continue to traverse the adjacent blood vessel segmentation points backward along the trajectory graph until the last blood vessel segmentation point in the trajectory graph is traversed, and the last blood vessel segmentation point is determined as the end point of the bypass.

[0238] Specifically, if the computer device determines that there is a main branch vessel mask point among all the vessel segmentation points between the first bifurcation point and the current second bifurcation point, then the adjacent vessel segmentation points can be traversed backward along the trajectory graph starting from each current second bifurcation point until the last vessel segmentation point on a different path in the trajectory graph is traversed, and the last vessel segmentation point is determined as the bypass tail point. The number of bypass tail points can be greater than 1.

[0239] It should be noted that if there are intersections after the current second bifurcation point in the trajectory graph, in this case the current second intersection can be used as the first bifurcation point, and the traversed intersections can be used as the current second bifurcation point, and the steps in S3231b and S3231c above are continued until the last blood vessel segmentation point in the trajectory graph is traversed to obtain the end point of the bypass.

[0240] Furthermore, the computer device can determine the bypass path through each bypass starting point, each corresponding bypass end point, and all the blood vessel segmentation points that have been traversed between each bypass starting point and each corresponding bypass end point. The number of bypass path trajectories can be greater than 1, and different bypass paths can share a bypass starting point, but different bypass end points.

[0241] Among them, all the traversed blood vessel segmentation points between the bypass starting point and the bypass ending point may be blood vessel segmentation points in the bypass path, or may not be blood vessel segmentation points in the bypass path. Fig.18 As shown, the starting point of the bridge in a trajectory diagram is point A, the intersection points after point A are point B and point C, the adjacent blood vessel segmentation points after point B are point D and point E, and the adjacent blood vessel segmentation points after point C are point F and point G. If there is no main branch vessel mask point among all the blood vessel segmentation points between point A and point B that have been traversed, and there is a main branch vessel mask point among all the blood vessel segmentation points between point A and point C that have been traversed, then point C can be retained, point B can be filtered out, and the traversal can be continued from point C backward. The final bridge path trajectory can be path ACF and / or path ACG, that is, the bridge path trajectory does not contain the filtered blood vessel segmentation points and the untraversed blood vessel segmentation points.

[0242] The above-mentioned bypass reconstruction method can determine the bypass tail point, and then obtain the bypass path trajectory through the bypass tail point, and process the bypass path trajectory and the anastomosis between the bypass and the normal blood vessel to obtain the bypass reconstruction result. This method avoids manual reconstruction of the bypass model, can save human resources and bypass reconstruction time, reduce the cost of bypass reconstruction, and further improve the efficiency of bypass reconstruction.

[0243] In some scenarios, if there are multiple second bifurcation points in the trajectory graph, and there are intersections after each second intersection, then after the above step S3231a is executed, Fig.19 As shown, the above-mentioned bridge reconstruction method may further include the following steps:

[0244] S3231d, if there are bridging mask points in all the blood vessel segmentation points between the first intersection point and each second intersection point, then starting from each second bifurcation point, continue to traverse the adjacent blood vessel segmentation points backward along the trajectory graph, and determine at least one next intersection point traversed as the third intersection point.

[0245] Specifically, the computer device may determine whether there is a blood vessel segmentation point belonging to the bridging mask among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point. If it is determined that there is a bridging mask point among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point, the computer device may start from the current second bifurcation point and continue to traverse the adjacent blood vessel segmentation points backward along the trajectory graph, and determine at least one next intersection point traversed as the third intersection point. The number of the third intersection points may be greater than or equal to 1.

[0246] In addition, if starting from the current second bifurcation point, the adjacent vascular segmentation points are traversed backward along the trajectory graph to determine that there is no intersection among the traversed adjacent vascular segmentation points, the total number of bridging mask points in the traversed adjacent vascular segmentation points can be obtained, and it can be determined whether the total number of bridging mask points is less than or equal to the preset number. If the total number of bridging mask points is less than or equal to the preset number, the current second bifurcation point can be discarded, and then starting from other second bifurcation points, the adjacent vascular segmentation points are traversed backward to determine the third intersection point.

[0247] S3231e: If there are bridging mask points among all the blood vessel segmentation points between the second intersection point and each third intersection point, determine the direction vector angle between the first intersection point and each second intersection point.

[0248] Specifically, the computer device can determine whether there is a blood vessel segmentation point belonging to the bridging mask among all the blood vessel segmentation points between the second intersection point and any third intersection point, and if it is determined that there is a bridging mask point among all the blood vessel segmentation points between the second intersection point and the current third intersection point, the direction vector angle between the first intersection point and the second intersection point adjacent to the current third intersection point can be calculated. The current third intersection point can be understood as any third intersection point among all the third intersection points.

[0249] It should be noted that there may be one or more non-intersection points between the second intersection point and the current third intersection point, or there may be no intersection point. The first intersection point and each second intersection point have a corresponding direction vector angle. Starting from the first intersection point, all the blood vessel segmentation points between the first intersection point and the current second intersection point (including the first intersection point and the current second intersection point) are numbered in sequence, namely blood vessel segmentation point 1, blood vessel segmentation point 2, blood vessel segmentation point 3, ..., blood vessel segmentation point n, wherein the first intersection point is the first blood vessel segmentation point 1, and the last blood vessel segmentation point n is the current second intersection point, then the direction vector angle between the first intersection point and the current second intersection point can be equal to the coordinate value of the first blood vessel segmentation point 1 between the first intersection point and the current second intersection point minus the coordinate value of the second blood vessel segmentation point 2, the coordinate value of the second blood vessel segmentation point 2 minus the coordinate value of the first blood vessel segmentation point 1, the coordinate value of the third blood vessel segmentation point 3 minus the coordinate value of the first blood vessel segmentation point 1, ..., and the coordinate value of the last blood vessel segmentation point n minus the coordinate value of the first blood vessel segmentation point 1, and then normalized to obtain a normalized result, and then calculate the cosine value of the normalized result, that is, the direction vector angle. The above blood vessel segmentation point can be a three-dimensional coordinate point, and the normalized result can be a direction vector.

[0250] S3231f. Determine the target second bifurcation point in the bridging trajectory according to the direction vector angle.

[0251] It should be noted that the computer device can compare the direction vector angles corresponding to the second intersection points, determine the minimum direction vector angle, and determine the second intersection point corresponding to the minimum direction vector angle as the target second bifurcation point in the bridge path. The number of the determined target second bifurcation points can be greater than or equal to 1.

[0252] At the same time, after the above-mentioned step S3231d is executed, the above-mentioned bypass reconstruction method may also include: if there are branch mask points in all the blood vessel segmentation points between the first bifurcation point and each second bifurcation point, then the target second bifurcation point in the bypass path trajectory is determined according to the maximum number of belonging mask points corresponding to all the blood vessel segmentation points between the first bifurcation point and the previous blood vessel segmentation point that has been traversed.

[0253] It is understandable that the computer device can determine whether there is a blood vessel segmentation point belonging to the branch mask among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point. If it is determined that there is a branch mask point among all the blood vessel segmentation points between the first bifurcation point and the current second bifurcation point, it can be determined whether there is the most attribution mask points between the first bifurcation point and the current second bifurcation point according to the most attribution mask points corresponding to all the blood vessel segmentation points between the first bifurcation point and the previous blood vessel segmentation point that has been traversed. If so, the current second intersection point is used as the target second bifurcation point in the bypass path trajectory. The previous blood vessel segmentation point that has been traversed can be the blood vessel segmentation point before the first intersection point.

[0254] Among them, if all the vascular segmentation points between the first bifurcation point and the last traversed vascular segmentation point include 7 vascular segmentation points, namely vascular segmentation point 1 (belonging to the left anterior descending branch mask point), vascular segmentation point 2 (belonging to the left anterior descending branch mask point), vascular segmentation point 3 (belonging to the diagonal branch mask point), vascular segmentation point 4 (belonging to the left anterior descending branch mask point), vascular segmentation point 5 (belonging to the anterior septal branch mask point), vascular segmentation point 6 (belonging to the diagonal branch mask point), and vascular segmentation point 7 (belonging to the anterior septal branch mask point), among which there are 3 left anterior descending branch mask points, 2 anterior septal branch mask points, and 2 diagonal branch mask points. Since the left anterior descending branch belongs to the three main branches, the most belonging mask points can be the main branch mask points.

[0255] S3231g, starting from the second target intersection point, continue to traverse the adjacent blood vessel segmentation points backward along the trajectory graph until the last blood vessel segmentation point in the trajectory graph is traversed, and the last blood vessel segmentation point is determined as the end point of the bypass.

[0256] Specifically, the computer device may start from any target second bifurcation point, continue to traverse the blood vessel segmentation points adjacent to the current target second bifurcation point backward along the trajectory graph, until traversing to the last blood vessel segmentation point on a different path in the trajectory graph, and determine the last blood vessel segmentation point as the bridge tail point. The current target second bifurcation point may be any target second intersection point among all target second intersection points.

[0257] The above-mentioned bridge reconstruction method can determine the bridge tail point, and then reconstruct the target bridge data according to the bridge tail point to obtain the bridge reconstruction result. This method avoids manual reconstruction of the bridge model, can save human resources and bridge reconstruction time, reduce the bridge reconstruction cost, and further improve the bridge reconstruction efficiency.

[0258] As one of the embodiments, Fig. 20 As shown, the step of obtaining the bridging reconstruction information based on the target blood vessel mask and the blood vessel segmentation result in the above S3200 can be implemented by the following steps:

[0259] S3240: Determine corresponding blood vessel segmentation points according to the blood vessel segmentation result.

[0260] Specifically, the above-mentioned blood vessel segmentation result may be blood vessel segmentation data. The blood vessel segmentation data may be in the form of point coordinates, which are displayed in a coordinate system, and each blood vessel segmentation result has a corresponding blood vessel segmentation point.

[0261] S3250, trace back each blood vessel segmentation point in sequence from the end point of the bypass to the starting point of the bypass along the bypass path, and determine the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point.

[0262] Specifically, the computer device can trace back each blood vessel segmentation point in sequence from the end point of each bypass path to the start point of each bypass path, and according to the target blood vessel mask of the traced blood vessel segmentation point, any traced blood vessel segmentation point can be determined as the anastomosis between the bypass and the normal blood vessel. The number of the determined anastomosis between the bypass and the normal blood vessel can be greater than or equal to 1.

[0263] Among them, the step of determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point in the above S3250 may include: if the end point of the bypass is a blood vessel mask point, and the current blood vessel segmentation point traced back is the bypass mask point, stopping the backtracking, and determining the current blood vessel segmentation point as the anastomosis between the bypass and the normal blood vessel.

[0264] It should be noted that if the backtracked bypass tail point is determined as a bypass mask point according to the target vessel mask of each vessel segmentation point, and the computer device stops backtracking when the current vessel segmentation point in the bypass path trajectory to which the bypass tail point belongs is a bypass mask point, then the computer device can determine the backtracked current vessel segmentation point as the anastomosis between the bypass and the normal vessel. The anastomosis between the bypass and the normal vessel can be a bifurcation point, that is, any intersection between the bypass tail point and the bypass start point.

[0265] In addition, in this embodiment, the vascular mask of the edge between two adjacent vascular segmentation points can also be determined according to the target vascular mask corresponding to each vascular segmentation point. The vascular mask of the edge between two adjacent vascular segmentation points can be the target vascular mask corresponding to the most belonging mask points among all vascular segmentation points between the two adjacent vascular segmentation points. The vascular mask of each edge can be a vascular mask or a bridging mask.

[0266] If the current blood vessel segmentation point (i.e., bifurcation point) is the end point of the bypass of the blood vessel mask, and the previous edge connected to the current blood vessel segmentation point is the bypass mask, the current blood vessel segmentation point can be determined to be the anastomosis between the bypass and the normal blood vessel. If the current blood vessel segmentation point is the end point of the bypass mask (i.e., bifurcation point), and the current blood vessel segmentation point contains the blood vessel mask, the current blood vessel segmentation point is determined to be the anastomosis between the bypass and the normal blood vessel.

[0267] Among them, the step of determining the anastomosis between the bypass and the normal blood vessel based on the target blood vessel mask of the blood vessel segmentation point in the above S3250 may also include: if the end point of the bypass is a bypass mask point, then backtracking is stopped when the current blood vessel segmentation point is a blood vessel mask point, and the current blood vessel segmentation point is determined as the anastomosis between the bypass and the normal blood vessel.

[0268] It can be understood that if the backtracked bypass tail point is determined to be a bypass mask point based on the target vessel mask of each vessel segmentation point, then the computer device can continue to backtrack forward and stop backtracking when the current vessel segmentation point in the bypass path trajectory to which the bypass tail point belongs is a vessel mask point. At this time, the current vessel segmentation point can be determined as the anastomosis between the bypass and the normal blood vessel.

[0269] At the same time, the step of determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point in the above S3250 may also include: if all blood vessel segmentation points between the current blood vessel segmentation point and the bypass tail point are bypass mask points, and the backtracking distance between the current blood vessel segmentation point and the bypass tail point is greater than the preset distance threshold, then the bypass tail point is determined as the anastomosis between the bypass and the normal blood vessel.

[0270] It is understandable that if all the vascular segmentation points (including the current vascular segmentation point and the bypass tail point) between the current vascular segmentation point and the bypass tail point traced back by the computer device are all bypass mask points, and the traced distance between the current vascular segmentation point and the bypass tail point is greater than or equal to the preset distance threshold, then the bypass tail point can be determined as the anastomosis between the bypass and the normal blood vessel. Among them, the current vascular segmentation point traced back and the corresponding bypass tail point are on the same bypass path trajectory. The above-mentioned preset distance threshold can be determined according to actual conditions, and the specific value is not limited. In this embodiment, the above-mentioned preset distance threshold can be equal to 3cm.

[0271] The above-mentioned bypass reconstruction method can determine the anastomosis between the bypass and the normal blood vessel, and then obtain the bypass reconstruction result by processing the anastomosis between the bypass and the normal blood vessel, the bypass starting point and the bypass end point. This method avoids manual reconstruction of the bypass model, can save human resources and bypass reconstruction time, reduce the cost of bypass reconstruction, and further improve the efficiency of bypass reconstruction.

[0272] It should be understood that although Figure 1-20 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-20 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0273] In one embodiment, Fig.21 As shown, a bridge reconstruction device is provided, comprising: a segmentation result acquisition module 21, a mask acquisition module 22, a bridge information acquisition module 23 and a reconstruction module 24, wherein:

[0274] The segmentation result acquisition module 21 is used to acquire the heart segmentation result and the blood vessel segmentation result;

[0275] A mask acquisition module 22, used to acquire a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0276] A bypass information acquisition module 23, for obtaining bypass reconstruction information based on the target blood vessel mask and the blood vessel segmentation result, wherein the bypass reconstruction information includes a bypass starting point and an anastomosis between the bypass and the normal blood vessel;

[0277] The reconstruction module 24 is used to process the bridging reconstruction information to obtain a bridging reconstruction result.

[0278] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0279] In one embodiment, the bridging information acquisition module 23 includes: a blood vessel centerline acquisition unit, a candidate point determination unit and a bridging start point determination unit, wherein:

[0280] A blood vessel centerline acquisition unit, used for acquiring the blood vessel centerline through the blood vessel segmentation result;

[0281] A candidate point determination unit, used for determining a bridging candidate point based on all blood vessel points on the blood vessel centerline;

[0282] The bridge starting point determination unit is used to determine the bridge starting point through the bridge candidate points.

[0283] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0284] In one embodiment, the candidate point determination unit includes: a blood vessel endpoint acquisition subunit, a target blood vessel point acquisition subunit and a candidate point acquisition subunit, wherein:

[0285] The blood vessel endpoint acquisition subunit is used to acquire the blood vessel point set corresponding to all blood vessel points on the blood vessel centerline, and acquire the blood vessel endpoints in the blood vessel point set;

[0286] A target blood vessel point acquisition subunit is used to determine a preset number of target blood vessel points including blood vessel endpoints in the blood vessel point set;

[0287] The candidate point acquisition subunit is used to determine the candidate points for bridging according to the target blood vessel points.

[0288] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0289] In one embodiment, the candidate point acquisition subunit includes: at least one of a first determination subunit, a second determination subunit, and a third determination subunit, wherein:

[0290] A first determining subunit is configured to determine the blood vessel endpoints in all the target blood vessel points as candidate blood vessel points when a second total number of the blood vessel points that are bypassed in all the target blood vessel points is greater than a third preset number threshold;

[0291] A second determination subunit is used to determine the blood vessel endpoints in all the target blood vessel points as candidate points for bridging when the distances between each target blood vessel point and the highest point in the heart chamber are greater than a second preset distance threshold;

[0292] The third determination subunit is configured to determine a blood vessel endpoint as a candidate point for bridging when the minimum distance between the blood vessel endpoint among all target blood vessel points and the aortic arch is less than a third preset distance threshold.

[0293] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0294] In one embodiment, the bridge starting point determination unit includes: a first connected domain determination subunit, a second connected domain determination subunit and an interval distance acquisition subunit, wherein:

[0295] A first connected domain determining subunit is used to determine a target blood vessel connected domain according to the blood vessel segmentation result;

[0296] A second connected domain determining subunit is used to determine the target vessel connected domain to which the candidate bridging point belongs according to the candidate bridging point and the target vessel connected domain;

[0297] An interval distance acquisition subunit is used to acquire the interval distance between two bridging candidate points in the target blood vessel connection domain;

[0298] The bridging starting point determination subunit is used to determine the bridging starting point according to the interval distance.

[0299] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0300] In one embodiment, the bridging starting point determination subunit includes: a first reservation subunit, a total number acquisition subunit, a second reservation subunit and a determination subunit, wherein:

[0301] A first retaining subunit is configured to retain, when the interval distance is less than a fourth preset distance threshold, a bypass candidate point closest to the aorta among the bypass candidate points corresponding to the interval distance, to obtain a first retained candidate point;

[0302] a total number acquisition subunit, configured to acquire, when the distance between the bypass candidate point in the target vessel connection domain and the outer surface of the heart chamber is greater than a fifth preset distance threshold, a third total number of bypass candidate points belonging to the left anterior descending branch mask and / or the diagonal branch mask from among all vessel segmentation points in the target vessel connection domain according to the target vessel mask; the target vessel mask includes the left anterior descending branch mask and / or the diagonal branch mask;

[0303] a second reserved subunit, configured to determine, when the third total number is greater than or equal to a fourth preset number threshold, the bridging candidate points belonging to the left anterior descending branch mask and the diagonal branch mask among all the vessel segmentation points in the target vessel connection domain as second reserved candidate points;

[0304] The determination subunit is used to determine the first reserved candidate point and the second reserved candidate point as the bridge starting point.

[0305] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0306] In one embodiment, the bridging information acquisition module 23 further includes: a blood vessel segmentation point determination unit and a segmentation point tracing unit, wherein:

[0307] Blood vessel segmentation point determination, used to determine the corresponding blood vessel segmentation point according to the blood vessel segmentation result;

[0308] The segmentation point tracing unit is used to trace back each blood vessel segmentation point in sequence from the end point of the bypass to the starting point of the bypass along the bypass path, and determine the anastomosis between the bypass and the normal blood vessel based on the target blood vessel mask of the traced blood vessel segmentation point.

[0309] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0310] In one embodiment, the segmentation point backtracking unit includes: a first backtracking subunit, wherein:

[0311] The first backtracking subunit is used to stop backtracking when the end point of the bypass is a blood vessel mask point and the current blood vessel segmentation point traced back is a bypass mask point, and determine the current blood vessel segmentation point as the anastomosis between the bypass and the normal blood vessel.

[0312] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0313] In one embodiment, the segmentation point backtracking unit further includes: a second backtracking subunit, wherein:

[0314] The second backtracking subunit is used to stop backtracking when the current blood vessel segmentation point is a blood vessel mask point when the end point of the bypass is a bypass mask point, and determine the current blood vessel segmentation point as the anastomosis between the bypass and the normal blood vessel.

[0315] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0316] In one embodiment, the segmentation point backtracking unit further includes: a third backtracking subunit, wherein:

[0317] The third backtracking subunit is used to determine the bypass tail point as the anastomosis between the bypass and the normal blood vessel when all blood vessel segmentation points between the current blood vessel segmentation point and the bypass tail point are bypass mask points and the backtracking distance between the current blood vessel segmentation point and the bypass tail point is greater than a sixth preset distance threshold.

[0318] The bridge reconstruction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0319] The specific definition of the bypass reconstruction device can be found in the definition of the bypass reconstruction method above, which will not be repeated here. Each module in the above-mentioned bypass reconstruction device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0320] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0321] Obtain heart segmentation results and blood vessel segmentation results;

[0322] Obtaining a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0323] Based on the target blood vessel mask and the blood vessel segmentation result, the bypass reconstruction information is obtained, and the bypass reconstruction information includes the starting point of the bypass and the anastomosis between the bypass and the normal blood vessel;

[0324] The bridge reconstruction information is processed to obtain a bridge reconstruction result.

[0325] In one embodiment, a readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0326] Obtain heart segmentation results and blood vessel segmentation results;

[0327] Obtaining a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0328] Based on the target blood vessel mask and the blood vessel segmentation result, the bypass reconstruction information is obtained, and the bypass reconstruction information includes the starting point of the bypass and the anastomosis between the bypass and the normal blood vessel;

[0329] The bridge reconstruction information is processed to obtain a bridge reconstruction result.

[0330] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0331] Obtain heart segmentation results and blood vessel segmentation results;

[0332] Obtaining a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model;

[0333] Based on the target blood vessel mask and the blood vessel segmentation result, the bypass reconstruction information is obtained, and the bypass reconstruction information includes the starting point of the bypass and the anastomosis between the bypass and the normal blood vessel;

[0334] The bridge reconstruction information is processed to obtain a bridge reconstruction result.

[0335] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0336] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0337] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A bridge reconstruction method, characterized in that: The method comprises: Obtain heart segmentation results and blood vessel segmentation results; Acquire a target blood vessel mask according to the heart segmentation result, the blood vessel segmentation result and the segmentation model; Based on the target blood vessel mask and the blood vessel segmentation result, obtaining bypass reconstruction information, the bypass reconstruction information including a bypass starting point, a bypass path, and an anastomosis between the bypass and a normal blood vessel; Processing the centerline information of the normal blood vessel and the bypass reconstruction information to obtain a bypass reconstruction result; The obtaining of the bypass reconstruction information based on the target blood vessel mask and the blood vessel segmentation result includes: determining the corresponding blood vessel segmentation point according to the blood vessel segmentation result, and determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point.

2. The method according to claim 1, characterized in that Based on the target blood vessel mask and the blood vessel segmentation result, the bypass reconstruction information is obtained, including: Acquire the blood vessel centerline through the blood vessel segmentation result; Determining candidate points for bridging based on all vascular points on the vascular centerline; The bridging starting point is determined based on the bridging candidate points.

3. The method according to claim 2, characterized in that Determining the candidate bridging points according to all the blood vessel points on the blood vessel centerline includes: Obtaining a blood vessel point set corresponding to all blood vessel points on the blood vessel centerline, and obtaining blood vessel endpoints in the blood vessel point set; Determine a preset number of target blood vessel points in the blood vessel point set including the blood vessel endpoint; The candidate bridging point is determined according to the target blood vessel point.

4. The method according to claim 3, characterized in that Determining the candidate bridging point according to the target blood vessel point includes at least one of the following situations: If a second total number of bypass vessel points among all target vessel points is greater than a third preset number threshold, determining the vessel endpoints among all target vessel points as bypass candidate points; If the distances between each of the target blood vessel points and the highest point in the heart chamber are greater than a second preset distance threshold, then determining the blood vessel endpoints in all the target blood vessel points as candidate points for bypassing; If the minimum distance between the blood vessel endpoint among all target blood vessel points and the aortic arch is less than a third preset distance threshold, the blood vessel endpoint is determined as a candidate point for bypass surgery.

5. The method according to any one of claims 2 to 4, characterized in that: Determining the bridging starting point based on the bridging candidate point includes: Determining a target blood vessel connected domain according to the blood vessel segmentation result; Determine the target blood vessel connected domain to which the candidate bridging point belongs according to the candidate bridging point and the target blood vessel connected domain; Obtaining the interval distance between any two bridging candidate points in the target blood vessel connection domain; The bridging starting point is determined according to the interval distance.

6. The method according to claim 5, characterized in that Determining the starting point of the bridge according to the interval distance includes: If the interval distance is less than a fourth preset distance threshold, retaining the bypass candidate point closest to the aorta among the bypass candidate points corresponding to the interval distance to obtain a first retained candidate point; If the distance between the candidate bridging point in the target vascular connection domain and the outer surface of the heart chamber is greater than a fifth preset distance threshold, obtaining, according to the target vascular mask, a third total number of candidate bridging points belonging to the left anterior descending branch mask and / or the diagonal branch mask among all vascular segmentation points in the target vascular connection domain; the target vascular mask includes the left anterior descending branch mask and / or the diagonal branch mask; If the third total number is greater than or equal to a fourth preset number threshold, determining the bridging candidate points belonging to the left anterior descending branch mask and the diagonal branch mask among all the vessel segmentation points in the target vessel connection domain as second retained candidate points; The first reserved candidate point and the second reserved candidate point are determined as the bridge starting points.

7. The method according to claim 1, characterized in that Determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point includes: Along the bypass path, each blood vessel segmentation point is traced back in sequence from the end point of the bypass to the starting point of the bypass, and the anastomosis between the bypass and the normal blood vessel is determined according to the target blood vessel mask of the blood vessel segmentation point.

8. The method according to claim 7, characterized in that Determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point traced back, including: If the end point of the bypass is a blood vessel mask point, and the current blood vessel segmentation point traced back is a bypass mask point, the backtracking is stopped, and the current blood vessel segmentation point is determined as the anastomosis between the bypass and the normal blood vessel.

9. The method according to claim 8, characterized in that Determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point, further comprising: If the end point of the bypass is the bypass mask point, then the backtracking stops when the current blood vessel segmentation point is the blood vessel mask point, and the current blood vessel segmentation point is determined as the anastomosis between the bypass and the normal blood vessel; And preferably, determining the anastomosis between the bypass and the normal blood vessel according to the target blood vessel mask of the blood vessel segmentation point further includes: If all the vessel segmentation points between the current vessel segmentation point and the bypass tail point are the bypass mask points, and the backtracking distance between the current vessel segmentation point and the bypass tail point is greater than the sixth preset distance threshold, the bypass tail point is determined as the anastomosis between the bypass and the normal blood vessel.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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