Image Processing Method and Apparatus, Computer-Readable Storage Medium, and Electronic Device

Abstract

This application relates to the field of image processing technology, and specifically relates to an image processing method, an image processing device, a computer-readable storage medium, and an electronic device. It solves the problem that the medical image sequence obtained by shooting with fixed sampling parameters cannot meet the requirements of being able to clearly see the details of some organs and reducing the number of images in the medical image sequence to save medical resources. The image processing method determines a reconstructed medical image sequence including N organ regions based on M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of the N organ regions, so that different organ regions in the reconstructed medical image sequence have different image sampling parameters, in order to meet the requirements of being able to clearly see the details of some organs and reducing the number of images in the medical image sequence to save medical resources.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular, to an image processing method, an image processing apparatus, a computer-readable storage medium, and an electronic device. Background Art

[0002] Medical image sequences are generally obtained by photographing a certain part of the human body. Existing photographing devices all use fixed sampling parameters to photograph medical image sequences. For example, a fixed sampling interval is used to photograph medical image sequences. Another example is that a fixed sampling slice thickness is used to photograph medical image sequences.

[0003] However, a certain part of the human body may contain multiple organs, and different organs have different requirements for sampling parameters. For example, if the sampling interval is too large, the details of some organs cannot be clearly seen in the medical image sequence. If the sampling interval is too small, the medical image sequence will include more images, resulting in a waste of medical resources. Therefore, the medical image sequences obtained by photographing with fixed sampling parameters cannot meet the requirements of being able to clearly see the details of some organs and reducing the number of images in the medical image sequence to save medical resources. Summary of the Invention

[0004] In view of this, embodiments of the present application provide an image processing method, an image processing apparatus, a computer-readable storage medium, and an electronic device, which solve the problem that the medical image sequences obtained by photographing with fixed sampling parameters cannot meet the requirements of being able to clearly see the details of some organs and reducing the number of images in the medical image sequence to save medical resources.

[0005] In a first aspect, an image processing method provided by an embodiment of the present application includes: determining the image sampling parameters of each of M medical image sequences, where the image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to the same part of the same subject, and this part includes N organ regions; determining the target sampling parameters of each of the N organ regions; and determining a reconstructed medical image sequence including the N organ regions based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions.

[0006] In connection with the first aspect of the present application, in some embodiments, based on M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of N organ regions, a reconstructed medical image sequence including the N organ regions is determined, including: for each organ region among the N organ regions, determining whether the respective image sampling parameters of the M medical image sequences include the target sampling parameter of the organ region; if so, selecting, from the M medical image sequences, the medical image sequences that meet the target sampling parameter conditions to obtain a first reconstructed medical image sequence; if not, performing a filling and segmentation operation on any one of the M medical image sequences based on the respective image sampling parameters of the N organ regions to obtain a second reconstructed medical image sequence; and determining a reconstructed medical image sequence including the N organ regions based on the first reconstructed medical image sequence and / or the second reconstructed medical image sequence.

[0007] In connection with the first aspect of the present application, in some embodiments, selecting, from the M medical image sequences, the medical image sequences that meet the target sampling parameter conditions to obtain a first reconstructed medical image sequence includes: for each organ region whose target sampling parameter is included in the image sampling parameters, obtaining the medical image sequence corresponding to the organ region in the medical image sequences corresponding to the image sampling parameters equal to the target sampling parameter of the organ region; and recombining the medical image sequences of each organ region whose target sampling parameter is included in the image sampling parameters into a first reconstructed medical image sequence.

[0008] In connection with the first aspect of the present application, in some embodiments, determining the respective target sampling parameters of the N organ regions includes: determining the project characteristic data of the clinical trial project participated by the subject; and determining the respective target sampling parameters of the N organ regions based on the project characteristic data.

[0009] In connection with the first aspect of the present application, in some embodiments, determining the respective target sampling parameters of the N organ regions based on the project characteristic data includes: determining the important organ regions and unimportant organ regions among the N organ regions based on the project characteristic data; and determining the respective target sampling parameters of the N organ regions based on the important organ regions and the unimportant organ regions.

[0010] In combination with the first aspect of the present application, in some embodiments, based on M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of N organ regions, a reconstructed medical image sequence including the N organ regions is determined, including: for the unimportant organ regions among the N organ regions, determining the medical image sequences that meet the interval extraction condition among the M medical image sequences; and based on the respective target sampling parameters of the N organ regions, performing an interval extraction operation on the medical image sequences that meet the interval extraction condition to determine a third reconstructed medical image sequence; for the important organ regions among the N organ regions, performing a filling and segmentation operation on any one of the M medical image sequences based on the respective image sampling parameters of the N organ regions to determine a fourth reconstructed medical image sequence; and based on the third reconstructed medical image sequence and the fourth reconstructed medical image sequence, determining the reconstructed medical image sequence of the N organ regions.

[0011] In combination with the first aspect of the present application, in some embodiments, performing a filling and segmentation operation on any one of the M medical image sequences based on the respective image sampling parameters of the N organ regions includes: performing interpolation processing on any one of the M medical image sequences to obtain a three-dimensional image sequence corresponding to the one medical image sequence; and performing segmentation on the three-dimensional image sequence based on the respective target sampling parameters of the N organ regions.

[0012] In combination with the first aspect of the present application, in some embodiments, the image sampling parameter includes any one of the following parameters: sampling interval, sampling slice thickness.

[0013] In a second aspect, an embodiment of the present application provides an image processing apparatus, including: an image sampling parameter determination module configured to determine the respective image sampling parameters of the M medical image sequences, where the respective image sampling parameters of the M medical image sequences are different from each other, and the M medical image sequences all correspond to N organ regions of the same subject; a target sampling parameter determination module configured to determine the respective target sampling parameters of the N organ regions; and a reconstruction module configured to determine a reconstructed medical image sequence including the N organ regions based on the M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of the N organ regions.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing instructions that, when executed by a processor of an electronic device, enable the electronic device to execute the image processing method mentioned in the first aspect above.

[0015] Fourthly, an embodiment of the present application provides an electronic device, which includes: a processor; a memory for storing computer-executable instructions; and the processor for executing the computer-executable instructions to implement the image processing method mentioned in the first aspect above.

[0016] For the image processing method provided by the embodiment of the present application, first determine the image sampling parameters of each of the M medical image sequences, where the image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to N organ regions of the same subject. Then determine the target sampling parameters of each of the N organ regions. Finally, based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions, determine a reconstructed medical image sequence including the N organ regions, so that different organ regions in the reconstructed medical image sequence have different image sampling parameters, so as to meet the requirements of being able to clearly see the details of some organs and reducing the number of images in the medical image sequence to save medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure shows a schematic diagram of an application scenario of the image processing method provided by an embodiment of the present application.

[0018] Figure 2 The figure shows a schematic flowchart of the image processing method provided by an embodiment of the present application.

[0019] Figure 2a The figure shows a schematic diagram of a medical image sequence provided by an embodiment of the present application.

[0020] Figure 3 The figure shows a schematic flowchart of the image processing method provided by another embodiment of the present application.

[0021] Figure 4 The figure shows a schematic flowchart of the image processing method provided by another embodiment of the present application.

[0022] Figure 5 The figure shows a schematic flowchart of the image processing method provided by another embodiment of the present application.

[0023] Figure 6 The figure shows a schematic flowchart of the image processing method provided by another embodiment of the present application.

[0024] Figure 7 The figure shows a schematic flowchart of the image processing method provided by another embodiment of the present application.

[0025] Figure 8 The figure shows a schematic flowchart of the image processing method provided by another embodiment of the present application.

[0026] Figure 9 The figure shows a schematic flowchart of an image processing method provided by another embodiment of the present application.

[0027] Figure 10 The figure shows a schematic structural diagram of an image processing apparatus provided by an embodiment of the present application.

[0028] Figure 11 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0029] Figure 12 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0030] Figure 13 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0031] Figure 14 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0032] Figure 15 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0033] Figure 16 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0034] Figure 17 The figure shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application.

[0035] Figure 18 The figure shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0036] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0037] Exemplary scenario

[0038] Figure 1 The figure shows a schematic diagram of an application scenario of an image processing method provided by an embodiment of the present application. Figure 1The scene shown includes a server 110 and an image acquisition device 120 communicatively connected to the server 110. Specifically, the image acquisition device 120 is used to acquire a medical image sequence and send the medical image sequence to the server 110. The server 110 is used to receive the medical image sequence sent by the image acquisition device 120, determine the image sampling parameters of each of the M medical image sequences, where the image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to the same part of the same subject, and this part includes N organ regions; determine the target sampling parameters of each of the N organ regions; based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions, determine a reconstructed medical image sequence including the N organ regions.

[0039] Exemplary method

[0040] Figure 2 The figure shows a schematic flowchart of an image processing method provided by an embodiment of the present application. As Figure 2 shown, the image processing method includes the following steps.

[0041] Step 210, determine the image sampling parameters of each of the M medical image sequences.

[0042] Specifically, M is a positive integer. The image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to the same part of the same subject, and this part includes N organ regions. The medical image sequence is captured by a medical imaging device, and the image sampling parameters of a medical image sequence captured by the medical imaging device are fixed. For example, the image sampling parameter can be the sampling interval. The sampling interval of a medical image sequence captured by the medical imaging device is fixed. The M medical image sequences all correspond to the N organ regions of the same subject, that is, the M medical image sequences are captured for the same part of the same subject, but the M medical image sequences are captured with different image sampling parameters.

[0043] In an embodiment of the present application, the image sampling parameter includes any one of the following parameters: sampling interval, sampling slice thickness. The sampling interval is the distance between two adjacent images in the medical image sequence. The sampling slice thickness is the thickness of each image in the medical image sequence. The image sampling parameter can also be the sampling pitch, sampling rate, etc., and the present application does not make specific limitations. The sampling pitch is the distance between the centers of the image thicknesses of two adjacent images in the medical image sequence. The sampling rate is the ratio of the sum of the sampling slice thicknesses of the medical image sequence to the sum of the sampling interval and the sampling slice thickness.

[0044] Exemplarily, as Figure 2aAs shown, 1 is the first frame image in a medical image sequence, 2 is the second frame image in the medical image sequence, 3 is the third frame image in the medical image sequence, 4 is the fourth frame image in the medical image sequence, m is the sampling slice thickness, n is the sampling interval, and p is the sampling pitch. The sampling rate is (4m) / (4m + 3n).

[0045] In practical applications, when shooting a medical image sequence, the method of setting the sampling interval equal to the sampling slice thickness is generally adopted. Therefore, generally, only the sampling interval or the sampling slice thickness needs to be set. That is, generally, as long as the sampling interval is set, it is defaulted that the sampling slice thickness is equal to the sampling interval, or as long as the sampling slice thickness is set, it is defaulted that the sampling interval is equal to the sampling slice thickness.

[0046] Step 220, determine the target sampling parameters for each of the N organ regions.

[0047] Specifically, different organ regions have different requirements for image sampling parameters. Therefore, the target sampling parameters for each of the N organ regions can be determined according to the requirements of different organ regions for image sampling parameters. That is, the target sampling parameters are determined according to the requirements of the organ regions for image sampling parameters. For example, the image sampling parameters required for the lung region are a sampling interval of 1 mm, and it can be determined that the target sampling parameter for the lung region is a sampling interval of 1 mm. Another example is that the image sampling parameters required for the leg region are a sampling interval of 5 mm, and it can be determined that the target sampling parameter for the leg region is a sampling interval of 5 mm.

[0048] Step 230, based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions, determine a reconstructed medical image sequence including the N organ regions.

[0049] Specifically, based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions, to determine a reconstructed medical image sequence including the N organ regions, it can be to select the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences according to the target sampling parameters of each of the N organ regions, and then recombine the selected medical image sequences that meet the target sampling parameter conditions, so as to obtain a reconstructed medical image sequence of the N organ regions. It can also be to fill the M medical image sequences and then re-segment them into medical image sequences that meet the target sampling parameter conditions, so as to obtain a reconstructed medical image sequence of the N organ regions.

[0050] The image processing method provided by the embodiments of the present application realizes the requirement of being able to see the details of some organs clearly and reducing the number of images in the medical image sequence to save medical resources.

[0051] Figure 3The following is a schematic flowchart of an image processing method provided by another embodiment of the present application. Based on Figure 2 the embodiment shown, an Figure 3 extended embodiment is derived. Figure 3 Below, the differences between the Figure 2 embodiment shown and the

[0052] embodiment shown will be emphasized. The similarities will not be elaborated again. Figure 3 As shown, in the embodiment of the present application, the step of determining a reconstructed medical image sequence including N organ regions based on M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of the N organ regions includes the following steps.

[0053] Step 310: For each of the N organ regions, determine whether the respective image sampling parameters of the M medical image sequences include the target sampling parameter of the organ region.

[0054] Specifically, if the respective image sampling parameters of the M medical image sequences include the target sampling parameter of the organ region, then execute Step 320, that is, select the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences to obtain the first reconstructed medical image sequence. If the respective image sampling parameters of the M medical image sequences do not include the target sampling parameter of the organ region, then execute Step 330, that is, perform a filling and segmentation operation on any one of the M medical image sequences based on the respective image sampling parameters of the N organ regions to obtain the second reconstructed medical image sequence.

[0055] Step 320: Select the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences to obtain the first reconstructed medical image sequence.

[0056] Step 330: Perform a filling and segmentation operation on any one of the M medical image sequences based on the respective image sampling parameters of the N organ regions to obtain the second reconstructed medical image sequence.

[0057] For example, M is equal to 3. N is equal to 2. The respective image sampling parameters of the 3 medical image sequences are: the sampling slice thickness is 1 mm, 3 mm, and 5 mm. The respective target sampling parameters of the 2 organ regions are: the sampling slice thickness is 3 mm and 4 mm.

[0058] For an organ region with a target sampling parameter of 3 mm, it can be seen that the target sampling parameter of this organ region is included in the image sampling parameters of each of the three medical image sequences. Therefore, step 320 can be executed, that is, multiple images corresponding to this organ region in the medical image sequence with an image sampling parameter of 3 mm are selected from the three medical image sequences to obtain the first reconstructed medical image sequence. That is, the first reconstructed medical image sequence is the medical image sequence with an image sampling parameter of 3 mm corresponding to the organ region with a target sampling parameter of 3 mm.

[0059] For an organ region with a target sampling parameter of 4 mm, it can be seen that the target sampling parameter of this organ region is not included in the image sampling parameters of each of the three medical image sequences. Therefore, step 330 can be executed, that is, a filling and segmentation operation is performed on any one of the three medical image sequences to obtain the second reconstructed medical image sequence. That is, the second reconstructed medical image sequence is the medical image sequence with an image sampling parameter of 4 mm corresponding to the organ region with a target sampling parameter of 4 mm.

[0060] Step 340: Based on the first reconstructed medical image sequence and / or the second reconstructed medical image sequence, determine a reconstructed medical image sequence including N organ regions.

[0061] For example, by combining the first reconstructed medical image sequence and the second reconstructed medical image sequence obtained above, a reconstructed medical image sequence including two organ regions with target sampling parameters of 3 mm and 4 mm respectively can be obtained.

[0062] Specifically, for each organ region in the N organ regions, step 310 needs to be executed. For example, for one organ region, after executing step 310, step 320 is executed, thereby obtaining the first reconstructed medical image sequence. For another organ region, after executing step 310, step 330 is executed, thereby obtaining the second reconstructed medical image sequence.

[0063] The image processing method provided in this embodiment can selectively choose the method for reconstructing the medical image sequence in a targeted manner, improving the efficiency of the reconstructed medical image sequence.

[0064] Figure 4 The following shows a schematic flowchart of an image processing method provided in another embodiment of the present application. Based on the Figure 3 embodiment shown, an Figure 4 embodiment is extended. The following focuses on Figure 4 the differences between the Figure 3 embodiment shown and the

[0065] embodiment shown. The same parts will not be elaborated again. Figure 4As shown, in the embodiments of the present application, the step of selecting a medical image sequence that meets the target sampling parameter conditions from M medical image sequences to obtain the first reconstructed medical image sequence includes the following steps.

[0066] Step 410: For each organ region whose target sampling parameter is included in the image sampling parameter, obtain the medical image sequence corresponding to the organ region in the medical image sequence corresponding to the image sampling parameter equal to the target sampling parameter of the organ region.

[0067] Step 420: Recombine the medical image sequences of each organ region whose target sampling parameter is included in the image sampling parameter into the first reconstructed medical image sequence.

[0068] For example, M is equal to 5. N is equal to 3. The image sampling parameters of the 5 medical image sequences are respectively: the sampling slice thickness is 1mm, 2mm, 3mm, 5mm, and 6mm. The target sampling parameters of the 3 organ regions are respectively: the sampling slice thickness is 1mm, 3mm, and 6mm.

[0069] For the organ region with a target sampling parameter of 1mm, obtain the medical image sequence corresponding to the organ region in the medical image sequence corresponding to the image sampling parameter equal to the target sampling parameter of the organ region, that is, obtain multiple images corresponding to the organ region with a target sampling parameter of 1mm in the medical image sequence with an image sampling parameter of 1mm.

[0070] For the organ region with a target sampling parameter of 3mm, obtain the medical image sequence corresponding to the organ region in the medical image sequence corresponding to the image sampling parameter equal to the target sampling parameter of the organ region, that is, obtain multiple images corresponding to the organ region with a target sampling parameter of 3mm in the medical image sequence with an image sampling parameter of 3mm.

[0071] For the organ region with a target sampling parameter of 6mm, obtain the medical image sequence corresponding to the organ region in the medical image sequence corresponding to the image sampling parameter equal to the target sampling parameter of the organ region, that is, obtain multiple images corresponding to the organ region with a target sampling parameter of 6mm in the medical image sequence with an image sampling parameter of 6mm.

[0072] Reconstruct the medical image sequences of each organ region in which the target sampling parameters are included in the image sampling parameters into a first reconstructed medical image sequence. For example, it can be to reconstruct multiple images corresponding to the organ regions with target sampling parameters of 1 mm in the medical image sequence with an image sampling parameter of 1 mm, multiple images corresponding to the organ regions with target sampling parameters of 3 mm in the medical image sequence with an image sampling parameter of 3 mm, and multiple images corresponding to the organ regions with target sampling parameters of 6 mm in the medical image sequence with an image sampling parameter of 6 mm into a first reconstructed medical image sequence.

[0073] For the image processing method provided in this embodiment, only selection and recombination are required, without complex calculations, with a small amount of calculation and high reconstruction efficiency.

[0074] Figure 5 The following shows a schematic flowchart of the image processing method provided in another embodiment of the present application. Figure 2 Based on the embodiment shown Figure 5 an extended embodiment is shown Figure 5 The differences between the embodiment shown Figure 2 and the embodiment shown will be mainly described below, and the same parts will not be elaborated.

[0075] As Figure 5 shown, in the embodiment of the present application, the steps of determining the target sampling parameters of each of the N organ regions include the following steps.

[0076] Step 510, determine the project characteristic data of the clinical trial project participated by the subject.

[0077] Specifically, the project characteristic data can be organ importance data or the requirements for sampling parameters in the clinical trial project.

[0078] Step 520, based on the project characteristic data, determine the target sampling parameters of each of the N organ regions.

[0079] Specifically, based on the project characteristic data, determining the target sampling parameters of each of the N organ regions can be to determine the target sampling parameters of each organ region according to the organ importance data. For example, for important organs, a smaller sampling interval can be used, and for unimportant organs, a larger sampling interval can be used. For another example, the clinical trial project is a project for testing an indication (such as the lungs). Therefore, the requirements for the sampling parameters of the lung region in the clinical trial project are higher, and the requirements for the sampling parameters of other organ regions are lower.

[0080] The image processing method provided in this embodiment can determine the target sampling parameters for each of the N organ regions according to the specific conditions of the clinical trial project, meeting the requirements of the clinical trial project for detailed viewing of the corresponding organs, and meeting the requirement of the clinical trial project to minimize the number of images in the medical image sequence for the organs that are not concerned about to save medical resources.

[0081] Figure 6 The following is a schematic flowchart of the image processing method provided in another embodiment of the present application. On the basis of the embodiment shown in Figure 5 extends the embodiment shown in Figure 6 The following focuses on describing Figure 6 the differences between the embodiment shown in and Figure 5 the embodiment shown in, and the same parts will not be elaborated.

[0082] As shown in Figure 6 In the embodiment of the present application, the step of determining the target sampling parameters for each of the N organ regions based on the project feature data includes the following steps.

[0083] Step 610, based on the project feature data, determine the important organ regions and unimportant organ regions among the N organ regions.

[0084] Specifically, the project feature data can be the indication data targeted by the clinical trial project. For example, if the indication data targeted by the clinical trial project is the head, then the head can be determined as the important organ region, and the neck adjacent to the head can be the unimportant organ region. The project feature data can also be the organ importance levels specified in the contract requirements or project charter of the clinical trial project. According to the organ importance levels, the important organ regions and unimportant organ regions can be determined. For example, the organ with the highest level in the organ importance levels is the important organ, and the others are unimportant organ regions.

[0085] Step 620, based on the important organ regions and unimportant organ regions, determine the target sampling parameters for each of the N organ regions.

[0086] Specifically, for important organs, a smaller sampling interval can be used, and for unimportant organs, a larger sampling interval can be used. The specific values of the target sampling parameters can be selected according to actual needs, and the present application does not make specific limitations.

[0087] The image processing method provided in this embodiment takes into account the characteristics of different organs, thereby determining more appropriate target sampling parameters for different organ regions and improving the accuracy of the target sampling parameters.

[0088] Figure 7 The following is a schematic flowchart of the image processing method provided in another embodiment of the present application. On the basis of the embodiment shown in Figure 6 extends the embodiment shown inFigure 7 For the embodiments shown below, the following will focus on Figure 7 the differences between the embodiments shown and Figure 6 the embodiments shown. The similarities will not be elaborated.

[0089] As Figure 7 shown, in the embodiments of the present application, the steps of determining a reconstructed medical image sequence including N organ regions based on M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of the N organ regions include the following steps.

[0090] Step 710: For the unimportant organ regions among the N organ regions, determine the medical image sequences in the M medical image sequences that meet the interval extraction condition.

[0091] Step 720: Based on the respective target sampling parameters of the N organ regions, perform an interval extraction operation on the medical image sequences that meet the interval extraction condition to determine a third reconstructed medical image sequence.

[0092] Specifically, a medical image sequence contains multiple frames of images. The interval extraction operation is to extract one frame of image at an interval of at least one frame of image for the multiple frames of images in the medical image sequence. For example, the multiple frames of images included in a medical image sequence are the first frame of image, the second frame of image, the third frame of image, the fourth frame of image, the fifth frame of image, and the sixth frame of image. For interval extraction of this medical image, it can be to extract the first frame of image, the third frame of image, and the fifth frame of image. The medical image sequences that meet the interval extraction condition refer to: among the M medical image sequences, the medical image sequences that can obtain the target sampling parameters through the interval extraction operation.

[0093] For example, M is equal to 3. The respective image sampling parameters of the 3 medical image sequences are: the sampling interval is 1 mm, 2 mm, and 5 mm, and the sampling slice thickness is equal to the sampling interval. The target sampling parameter of the unimportant organ region is 6 mm. Therefore, it can be determined that the medical image sequence with a sampling interval of 2 mm meets the interval extraction condition, and only one frame of image needs to be extracted at an interval of one frame of image in the medical image sequence with a sampling interval of 2 mm. That is, for a general medical image sequence with a sampling slice thickness equal to the sampling interval, the interval between the first frame of image and the third frame of image in the medical image sequence with a sampling slice thickness of 2 mm is 6 mm. Therefore, as long as one frame of image is extracted at an interval of one frame of image, a medical image sequence with a sampling interval of 6 mm can be obtained.

[0094] In practical applications, the interval extraction condition of the target sampling parameter can be preset. For example, if the target sampling parameter is 6 mm, the interval extraction condition is: both the sampling interval and the sampling slice thickness of the medical image sequence are 2 mm.

[0095] The image processing method provided in this embodiment can reduce the sampling rate of unimportant organ regions, improve the loading speed of medical image sequences, and reduce the number of images in the medical image sequences to save medical resources.

[0096] Figure 8 The following is a schematic flowchart of an image processing method provided in another embodiment of the present application. On the basis of the embodiment shown in Figure 7 an extended embodiment is derived. Figure 8 The following focuses on describing Figure 8 the differences between the embodiment shown in Figure 7 and the embodiment shown in

[0097] As Figure 8 shown, in the embodiment of the present application, the steps of determining a reconstructed medical image sequence including N organ regions based on M medical image sequences, the respective image sampling parameters of the M medical image sequences, and the respective target sampling parameters of the N organ regions include the following steps.

[0098] Step 810: For important organ regions among the N organ regions, based on the respective image sampling parameters of the N organ regions, perform a filling and segmentation operation on any one of the M medical image sequences to determine a fourth reconstructed medical image sequence.

[0099] Specifically, for important organ regions among the N organ regions, one of the M medical image sequences can be selected, and then a filling and segmentation operation is performed on this medical image sequence, that is, first fill this medical image sequence into a three-dimensional image, and then intercept the required images in the three-dimensional image according to the required target sampling parameters to determine the reconstructed medical image sequence.

[0100] Step 820: Based on the third reconstructed medical image sequence and the fourth reconstructed medical image sequence, determine the reconstructed medical image sequence of the N organ regions.

[0101] Specifically, the third reconstructed medical image sequence and the fourth reconstructed medical image sequence are recombined together to obtain the reconstructed medical image sequence of the N organ regions. The third reconstructed medical image sequence is obtained after performing step 720.

[0102] The image processing method provided in this embodiment can segment a medical image sequence with any required target sampling parameters, has no requirements for the image sampling parameters of the M medical image sequences, reduces the requirements for the M medical image sequences, and improves the user experience.

[0103] Figure 9 The following is a schematic flowchart of an image processing method provided in another embodiment of the present application. In Figure 3 orFigure 6 Based on the illustrated embodiments, Figure 9 the following embodiments are elaborated. Figure 9 The differences between the illustrated embodiments and Figure 3 or Figure 6 the illustrated embodiments will be emphasized, while the similarities will not be elaborated further.

[0104] For example, Figure 9 as shown, in the embodiments of the present application, the steps of performing filling segmentation on any one of the M medical image sequences based on the respective image sampling parameters of N organ regions include the following steps.

[0105] Step 910: Perform interpolation processing on any one of the M medical image sequences to obtain a three-dimensional image sequence corresponding to the one medical image sequence.

[0106] Specifically, interpolation processing uses the gray values of known neighboring pixel points to generate the gray values of unknown pixel points, so as to regenerate an image with higher resolution from the original image. Performing interpolation processing on any one of the M medical image sequences can obtain a three-dimensional image sequence with higher resolution based on one medical image sequence.

[0107] Step 920: Segment the three-dimensional image sequence based on the respective target sampling parameters of N organ regions.

[0108] For example, the respective target sampling parameters of 2 organ regions are: the sampling slice thickness is 1 mm and 3 mm, and the sampling interval is equal to the sampling slice thickness. The three-dimensional image sequence is a three-dimensional image. For the organ region with a target sampling parameter of a sampling slice thickness of 1 mm, segmenting the three-dimensional image sequence can be to cut out slices with a thickness of 1 mm in the three-dimensional image sequence, then with an interval of 1 mm, and then cut out slices with a thickness of 1 mm until all slices including the organ region are obtained. For the organ region with a target sampling parameter of a sampling slice thickness of 3 mm, segmenting the three-dimensional image sequence can be to cut out slices with a thickness of 3 mm in the three-dimensional image sequence, then with an interval of 3 mm, and then cut out slices with a thickness of 3 mm until all slices including the organ region are obtained.

[0109] The image processing method provided in this embodiment can obtain a three-dimensional image sequence with higher resolution than the one medical image sequence, and can segment medical image sequences with any required target sampling parameters, improving the resolution of important organ regions in the medical image sequence, enabling users to clearly see the details of important organ regions, and being able to reduce the resolution of unimportant organ regions in the medical image sequence, improving the loading speed of the reconstructed medical image sequence.

[0110] As described above in combination with Figures 1 to 9, which describes in detail the method embodiments of the present application. Below, in conjunction with Figures 10 to 17 , the apparatus embodiments of the present application will be described in detail. It should be understood that the descriptions of the method embodiments correspond to those of the apparatus embodiments. Therefore, for the parts not described in detail, reference may be made to the previous method embodiments.

[0111] Exemplary device

[0112] Figure 10 The following shows a schematic structural diagram of an image processing apparatus provided by an embodiment of the present application. As Figure 11 shown, the image processing apparatus 1000 of the embodiment of the present application includes an image sampling parameter determination module 1010, a target sampling parameter determination module 1020, and a reconstruction module 1030.

[0113] Specifically, the image sampling parameter determination module 1010 is configured to determine the image sampling parameters of each of the M medical image sequences, where the image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to the same part of the same subject, and this part includes N organ regions. The target sampling parameter determination module 1020 is configured to determine the target sampling parameters of each of the N organ regions. The reconstruction module 1030 is configured to determine a reconstructed medical image sequence including the N organ regions based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions.

[0114] Figure 11 The following shows a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application. On the basis of the embodiment shown in Figure 10 extends the embodiment shown in Figure 11 The following focuses on describing Figure 11 the differences between the embodiment shown in Figure 10 and the embodiment shown in

[0115] As Figure 11 shown, the reconstruction module 1030 of the embodiment of the present application includes a judgment unit 1031, a first reconstruction unit 1032, a second reconstruction unit 1033, and a comprehensive reconstruction unit 1034.

[0116] Specifically, the determination unit 1031 is configured to determine, for each of the N organ regions, whether the image sampling parameters of each of the M medical image sequences include the target sampling parameters of the organ region. The first reconstruction unit 1032 is configured to, if so, select the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences to obtain the first reconstructed medical image sequence. The second reconstruction unit 1033 is configured to, if not, perform a filling and segmentation operation on any one of the M medical image sequences based on the image sampling parameters of each of the N organ regions to obtain the second reconstructed medical image sequence. The comprehensive reconstruction unit 1034 is configured to determine the reconstructed medical image sequence including the N organ regions based on the first reconstructed medical image sequence and / or the second reconstructed medical image sequence.

[0117] Figure 12 The following is a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application. Based on the Figure 11 embodiment shown, an Figure 12 extended embodiment is derived. The following focuses on Figure 12 the differences between the embodiment shown and the Figure 11 embodiment shown, and the same parts will not be described again.

[0118] As Figure 12 shown, the first reconstruction unit 1032 of the embodiment of the present application includes a selection subunit 1210 and a recombination subunit 1220.

[0119] Specifically, the selection subunit 1210 is configured to, for each organ region whose target sampling parameters are included in the image sampling parameters, obtain the medical image sequence corresponding to the organ region in the medical image sequence corresponding to the image sampling parameters equal to the target sampling parameters of the organ region. The recombination subunit 1220 is configured to recombine the medical image sequences of each organ region whose target sampling parameters are included in the image sampling parameters into the first reconstructed medical image sequence.

[0120] Figure 13 The following is a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application. Based on the Figure 10 embodiment shown, an Figure 13 extended embodiment is derived. The following focuses on Figure 13 the differences between the embodiment shown and the Figure 10 embodiment shown, and the same parts will not be described again.

[0121] As Figure 13 shown, the target sampling parameter determination module 1020 of the embodiment of the present application includes an item feature data determination unit 1021 and a target sampling parameter determination unit 1022.

[0122] Specifically, the project feature data determination unit 1021 is configured to determine the project feature data of the clinical trial project participated by the subject. The target sampling parameter determination unit 1022 is configured to determine the target sampling parameters of each of the N organ regions based on the project feature data.

[0123] Figure 14 The following is a schematic structural diagram of an image processing device provided by another embodiment of the present application. Figure 13 Based on the embodiment shown, Figure 14 the following embodiment is extended. Figure 14 The differences between the embodiment shown Figure 13 and the embodiment shown will be mainly described below, and the same parts will not be repeated.

[0124] As Figure 14 shown, the target sampling parameter determination unit 1022 of the embodiment of the present application includes an importance determination subunit 1410 and a target sampling parameter determination subunit 1420.

[0125] Specifically, the importance determination subunit 1410 is configured to determine the important organ regions and unimportant organ regions among the N organ regions based on the project feature data. The target sampling parameter determination subunit 1420 is configured to determine the target sampling parameters of each of the N organ regions based on the important organ regions and unimportant organ regions.

[0126] Figure 15 The following is a schematic structural diagram of an image processing device provided by another embodiment of the present application. Figure 14 Based on the embodiment shown, Figure 15 the following embodiment is extended. Figure 15 The differences between the embodiment shown Figure 14 and the embodiment shown will be mainly described below, and the same parts will not be repeated.

[0127] As Figure 15 shown, the reconstruction module 1030 of the embodiment of the present application includes an interval extraction determination unit 1035 and an interval extraction operation unit 1036.

[0128] Specifically, the interval extraction determination unit 1035 is configured to determine the medical image sequences that meet the interval extraction conditions in the M medical image sequences for the unimportant organ regions among the N organ regions. The interval extraction operation unit 1036 is configured to perform an interval extraction operation on the medical image sequences that meet the interval extraction conditions based on the target sampling parameters of each of the N organ regions to determine the third reconstructed medical image sequence.

[0129] Figure 16 The following is a schematic structural diagram of an image processing device provided by another embodiment of the present application. Figure 15 Based on the embodiment shown,Figure 16 In the illustrated embodiment, the following will focus on Figure 16 the differences between the illustrated embodiment and Figure 15 the illustrated embodiment. The similarities will not be elaborated.

[0130] As Figure 16 shown, the reconstruction module 1030 of the embodiment of the present application includes a filling and segmentation unit 1037 and a combination unit 1038.

[0131] Specifically, the filling and segmentation unit 1037 is configured to perform a filling and segmentation operation on any one of the M medical image sequences based on the respective image sampling parameters of the N organ regions for the important organ regions among the N organ regions, so as to determine a fourth reconstructed medical image sequence. The combination unit 1038 is configured to determine the reconstructed medical image sequences of the N organ regions based on the third reconstructed medical image sequence and the fourth reconstructed medical image sequence.

[0132] Figure 17 Shown is a schematic structural diagram of an image processing apparatus provided by another embodiment of the present application. Based on Figure 11 or Figure 16 the illustrated embodiment, the extended Figure 17 illustrated embodiment is presented. The following will focus on Figure 17 the differences between the illustrated embodiment and Figure 11 or Figure 16 the illustrated embodiment. The similarities will not be elaborated.

[0133] As Figure 17 shown, the filling and segmentation unit 1037 of the embodiment of the present application includes a three-dimensional image acquisition subunit 1710 and a segmentation subunit 1720.

[0134] Specifically, the three-dimensional image acquisition subunit 1710 is configured to perform interpolation processing on any one of the M medical image sequences to obtain a three-dimensional image sequence corresponding to the one medical image sequence. The segmentation subunit 1720 is configured to segment the three-dimensional image sequence based on the respective target sampling parameters of the N organ regions.

[0135] Exemplary electronic device

[0136] Figure 18 Shown is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 18 shown, the electronic device 180 includes: one or more processors 1801 and a memory 1802; and computer program instructions stored in the memory 1802, and when the computer program instructions are run by the processor 1801, the processor 1801 is caused to execute the image processing method of any of the above embodiments.

[0137] The processor 1801 can be a central processing unit (CPU) or other forms of processing units with image processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0138] The memory 1802 can include one or more computer program products, and the computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, random access memory (RAM) and / or cache memory, etc. Non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage media, and the processor 1801 can run the program instructions to implement the steps in the image processing methods of the various embodiments of the present application above and / or other desired functions.

[0139] In one example, the electronic device 180 may further include: an input device 1803 and an output device 1804, and these components are interconnected through a bus system and / or other forms of connection mechanisms ( Figure 18 not shown in the figure).

[0140] In addition, the input device 1803 may further include, for example, a keyboard, a mouse, a microphone, etc.

[0141] The output device 1804 can output various information to the outside. The output device 1804 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0142] Of course, for simplicity, Figure 18 only some of the components related to the present application in the electronic device 180 are shown in the figure, and components such as buses, input device / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 180 may further include any other appropriate components.

[0143] Exemplary computer-readable storage medium

[0144] In addition to the above methods and devices, the embodiments of the present application may also be computer program products, including computer program instructions, and the computer program instructions, when run by a processor, cause the processor to execute the steps in the image processing method of any of the above embodiments.

[0145] A computer program product may be written in any combination of one or more programming languages for executing the program code of the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0146] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are run by a processor, the processor is caused to execute the steps in the image processing method according to various embodiments of the present application described in the "Exemplary Method" section above of this specification.

[0147] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0148] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for the purposes of illustration and facilitating understanding, rather than limitations. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0149] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way. Words such as "including", "comprising", "having", etc. are open-ended terms, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the phrase "and / or", and can be used interchangeably with it, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0150] It should also be noted that in the devices, equipment, and methods of this application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this application.

[0151] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0152] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

[0153] The above are only the preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent replacements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims

1. An image processing method, characterized in that, Including: Determine the image sampling parameters of each of the M medical image sequences, where the image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to the same part of the same subject, and the part includes N organ regions; Determine the target sampling parameters of each of the N organ regions, where the target sampling parameters are determined according to the requirements of each of the N organ regions for the image sampling parameters; Based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions, determine a reconstructed medical image sequence including the N organ regions; The determining a reconstructed medical image sequence including the N organ regions based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions includes: For each of the N organ regions, Judge whether the target sampling parameters of the organ region are included in the image sampling parameters of each of the M medical image sequences; If so, select the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences to obtain a first reconstructed medical image sequence; If not, perform a filling and segmentation operation on any one of the M medical image sequences based on the image sampling parameters of each of the N organ regions to obtain a second reconstructed medical image sequence; Based on the first reconstructed medical image sequence and / or the second reconstructed medical image sequence, determine a reconstructed medical image sequence including the N organ regions.

2. The image processing method according to claim 1, wherein The selecting the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences to obtain a first reconstructed medical image sequence includes: For each of the organ regions whose target sampling parameters are included in the image sampling parameters, obtain the medical image sequences corresponding to the organ regions in the medical image sequences corresponding to the image sampling parameters equal to the target sampling parameters of the organ regions; Recombine the medical image sequences of each of the organ regions whose target sampling parameters are included in the image sampling parameters into a first reconstructed medical image sequence.

3. The image processing method according to claim 1, characterized in that The determining the target sampling parameters of each of the N organ regions includes: Determine the project characteristic data of the clinical trial project participated by the subject; Based on the project characteristic data, determine the target sampling parameters of each of the N organ regions.

4. The image processing method according to claim 3, wherein The determining the target sampling parameters of each of the N organ regions based on the project characteristic data includes: Based on the project characteristic data, determine the important organ regions and unimportant organ regions among the N organ regions; Based on the important organ regions and the unimportant organ regions, determine the target sampling parameters of each of the N organ regions.

5. The image processing method according to claim 4, wherein The determining a reconstructed medical image sequence including the N organ regions based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions includes: For the unimportant organ regions among the N organ regions, determine the medical image sequences that meet the interval extraction conditions in the M medical image sequences; and perform interval extraction operations on the medical image sequences that meet the interval extraction conditions based on the target sampling parameters of each of the N organ regions to determine the third reconstructed medical image sequence; For the important organ regions among the N organ regions, perform filling and segmentation operations on any one of the M medical image sequences based on the image sampling parameters of each of the N organ regions to determine the fourth reconstructed medical image sequence; Based on the third reconstructed medical image sequence and the fourth reconstructed medical image sequence, determine the reconstructed medical image sequences of the N organ regions.

6. The image processing method according to claim 1 or 5, characterized in that The performing filling and segmentation operations on any one of the M medical image sequences based on the image sampling parameters of each of the N organ regions includes: Perform interpolation processing on any one of the M medical image sequences to obtain a three-dimensional image sequence corresponding to this one medical image sequence; Segment the three-dimensional image sequence based on the target sampling parameters of each of the N organ regions.

7. The image processing method according to any one of claims 1 to 5, characterized in that The image sampling parameters include any one of the following parameters: sampling interval, sampling slice thickness.

8. An image processing apparatus, characterized in that, Include: An image sampling parameter determination module configured to determine the image sampling parameters of each of the M medical image sequences, where the image sampling parameters of each of the M medical image sequences are different from each other, and the M medical image sequences all correspond to N organ regions of the same subject; A target sampling parameter determination module configured to determine the target sampling parameters of each of the N organ regions, where the target sampling parameters are determined according to the requirements of each of the N organ regions for the image sampling parameters; A reconstruction module configured to determine the reconstructed medical image sequences including the N organ regions based on the M medical image sequences, the image sampling parameters of each of the M medical image sequences, and the target sampling parameters of each of the N organ regions; The reconstruction module includes a judgment unit, a first reconstruction unit, a second reconstruction unit, and a comprehensive reconstruction unit, The judgment unit is configured to, for each organ region among the N organ regions, judge whether the target sampling parameter of the organ region is included in the image sampling parameters of each of the M medical image sequences; The first reconstruction unit is configured to, if so, select the medical image sequences that meet the target sampling parameter conditions from the M medical image sequences to obtain the first reconstructed medical image sequence; The second reconstruction unit is configured to, if not, perform filling and segmentation operations on any one of the M medical image sequences based on the image sampling parameters of each of the N organ regions to obtain the second reconstructed medical image sequence; The comprehensive reconstruction unit is configured to determine the reconstructed medical image sequences including the N organ regions based on the first reconstructed medical image sequence and / or the second reconstructed medical image sequence.

9. A computer-readable storage medium, characterized in that, The storage medium stores instructions that, when executed by a processor of an electronic device, enable the electronic device to execute the image processing method according to any one of claims 1 to 7 above.

10. An electronic device, characterized in that, The electronic device includes: a processor; a memory for storing computer-executable instructions; The processor is configured to execute the computer-executable instructions to implement the image processing method according to any one of claims 1 to 7 above.

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