Tumor positioning method, electronic equipment and storage medium

Abstract

The invention provides a tumor positioning method, electronic equipment and a storage medium, relates to the technical field of medical treatment, in particular to the technical field of radiotherapy, and is used for solving the problem of low efficiency and accuracy of tumor positioning in the prior art. The method comprises the following steps: acquiring a plan image and a real-time image of a reference object associated with a movement track of a tumor to be positioned; determining boundary mask information of the reference object according to the plan image; determining deformation information of the reference object according to the plan image and the real-time image; determining a motion track of the reference object according to the boundary mask information and the deformation information; and positioning and tracking the to-be-positioned tumor according to the motion trail of the reference object.

Description

Technical Field

[0001] The present disclosure relates to the field of medical technology, in particular to the field of radiotherapy technology, and specifically to a tumor positioning method, electronic equipment and storage medium. Background Art

[0002] At present, one of the key technologies of radiotherapy is to maintain accurate positioning of the tumor during treatment. Especially for tumors that move with breathing, such as lung, liver, and pancreatic tumors, it is very difficult to accurately position the tumor at every moment due to the extremely low contrast of the tumor in the projection image and the interference of the surrounding tissue.

[0003] In the process of tumor localization, some other human tissues (which can be called substitutes, reference objects, surrogates, etc.) in the projection image, such as the diaphragm, chest wall, etc., usually have good contrast and clarity. In addition, their movement trajectories are highly correlated with the movement of the tumor. Therefore, these substitutes can be used for tumor localization and tracking.

[0004] The general surrogate tracking method is usually based on the gradient information of the acquired image, and uses a polynomial to fit the boundary of the surrogate to determine the motion trajectory of the surrogate, and then perform tumor localization tracking. However, the polynomial fitting of the surrogate boundary requires manual initialization of the polynomial coefficients, and when performing tumor localization tracking, it is necessary to train a priori model of the surrogate's motion trajectory. The process is cumbersome and the stability is poor, which leads to low efficiency and accuracy of tumor localization. Summary of the invention

[0005] The present disclosure provides a tumor localization method, an electronic device, and a storage medium, which can improve the efficiency and accuracy of tumor localization.

[0006] In a first aspect, the present disclosure provides a tumor localization method, comprising: acquiring a planned image and a real-time image of a reference object that is associated with a motion trajectory of the tumor to be located; determining boundary mask information of the reference object based on the planned image; determining deformation information of the reference object based on the planned image and the real-time image; determining the motion trajectory of the reference object based on the boundary mask information and the deformation information; and locating and tracking the tumor to be located based on the motion trajectory of the reference object.

[0007] In some embodiments, determining boundary mask information of a reference object according to a planning image includes: determining a three-dimensional mask of the reference object according to the planning image; converting the three-dimensional mask into a two-dimensional mask, and morphologically reconstructing the two-dimensional mask to obtain boundary mask information.

[0008] In some embodiments, the planning image includes multiple layers of planning sub-images of the reference object continuously acquired along a preset anatomical direction; determining a three-dimensional mask of the reference object based on the planning image includes: for each layer of the planning sub-image, determining a reference area to which the reference object belongs based on the planning sub-image; performing polynomial fitting on the gradient information of the reference area in the planning sub-image to obtain the boundary information of the reference object in the planning sub-image; and combining the boundary information of the reference object corresponding to each layer of the planning sub-image to obtain the three-dimensional mask of the reference object.

[0009] In some embodiments, a two-dimensional mask is morphologically reconstructed to obtain boundary mask information, including: performing corrosion processing and smoothing processing on the two-dimensional mask in sequence to obtain a processed two-dimensional mask; determining a boundary mask of a reference object in a first direction based on gradient information of the processed two-dimensional mask; and performing expansion processing in a second direction of the reference object based on the boundary mask and structural elements of the reference object in the first direction to obtain boundary mask information.

[0010] In some embodiments, the boundary mask information includes multiple sub-masks of a reference object; determining the motion trajectory of the reference object based on the boundary mask information and the deformation information includes: for each sub-mask segment, determining the deformation amount of the sub-mask based on the sub-mask and the deformation information corresponding to the sub-mask; determining the motion trajectory of the reference object based on the deformation amount of each sub-mask segment.

[0011] In some embodiments, positioning and tracking the tumor to be located is performed according to the motion trajectory of the reference object, including: obtaining a motion trajectory mapping model; the motion trajectory mapping model is used to represent the mapping relationship between the motion trajectory of the reference object and the motion trajectory of the tumor to be located; positioning and tracking the tumor to be located according to the motion trajectory of the reference object and the motion trajectory mapping model.

[0012] In some embodiments, the motion trajectory of the reference object includes the actual motion amount of the reference object at each motion point; based on the motion trajectory of the reference object, the tumor to be located is tracked, including: obtaining the normal motion amount of the reference object; based on the actual motion amount and normal motion amount of the reference object at each motion point, the tumor to be located is tracked.

[0013] In some embodiments, the reference object includes at least one of the following: diaphragm, lung wall, lung apex.

[0014] In a second aspect, the present disclosure further provides an electronic device, comprising: a processor and a memory configured to store processor executable instructions; wherein the processor is configured to execute the instructions to implement any one of the optional tumor localization methods in the first aspect above.

[0015] In a third aspect, the present disclosure further provides a non-volatile storage medium having a computer program stored thereon, and when the computer program is read and executed, any one of the optional tumor localization methods in the first aspect is implemented.

[0016] In the tumor localization method provided by the present disclosure, the boundary mask information of the reference object can be determined according to the planned image, and the deformation information of the reference object can be determined according to the planned image and the real-time image. Then, the motion trajectory of the reference object can be determined according to the boundary mask information and the deformation information, so as to facilitate the subsequent positioning and tracking of the tumor to be located according to the motion trajectory of the reference object. In this way, the present application does not need to initialize the polynomial coefficients through manual intervention, nor does it need to train the prior model, which improves the efficiency and accuracy of tumor localization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0018] Figure 1 A schematic diagram of a scenario of a tumor localization system provided by an embodiment of the present disclosure;

[0019] Figure 2 A schematic diagram of a flow chart of a tumor localization method provided in an embodiment of the present disclosure;

[0020] Figure 3 A schematic diagram of a flow chart of another tumor localization method provided in an embodiment of the present disclosure;

[0021] Figure 4 A schematic diagram of a scenario of a tumor localization method provided in an embodiment of the present disclosure;

[0022] Figure 5 A schematic diagram of a flow chart of another tumor localization method provided in an embodiment of the present disclosure;

[0023] Figure 6 A schematic diagram of a flow chart of another tumor localization method provided in an embodiment of the present disclosure;

[0024] Figure 7 A schematic diagram of a flow chart of another tumor localization method provided in an embodiment of the present disclosure;

[0025] Figure 8 A schematic diagram of a flow chart of another tumor localization method provided in an embodiment of the present disclosure;

[0026] Fig. 9 A schematic diagram of the structure of a tumor localization device provided in an embodiment of the present disclosure;

[0027] Fig.10 A schematic block diagram of an electronic device provided in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0029] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present disclosure. In addition, the terms "first", "second", and "third" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0030] In the description of the present disclosure, the word "exemplary" is used to mean "used as an example, illustration, or illustration". Any embodiment described in the present disclosure as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present disclosure. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present disclosure can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present disclosure with unnecessary details. Therefore, the present disclosure is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present disclosure.

[0031] It should be noted that since the method of the embodiment of the present disclosure is executed in a tumor localization device, the processing objects of each tumor localization device exist in the form of data or information, such as time, which is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data, so that the tumor localization device can process them. The details are not repeated here.

[0032] As described in the background art, in the process of tumor localization, some other human tissues (which can be called substitutes, reference objects, surrogates, etc.) in the projection image, such as the diaphragm, chest wall, etc., usually have good contrast and clarity. In addition, their motion trajectories are highly correlated with the movement of the tumor, so these substitutes can be used to locate and track the tumor.

[0033] The general surrogate tracking method is usually based on the gradient information of the acquired image, and uses a polynomial to fit the boundary of the surrogate to determine the motion trajectory of the surrogate, and then perform tumor localization tracking. However, the polynomial fitting of the surrogate boundary requires manual initialization of the polynomial coefficients, and when performing tumor localization tracking, it is necessary to train a priori model of the surrogate's motion trajectory. The process is cumbersome and the stability is poor, which leads to low efficiency and accuracy of tumor localization.

[0034] Based on the above technical problems, the disclosed embodiment provides a tumor localization method, which can determine the boundary mask information of the reference object according to the planned image, and determine the deformation information of the reference object according to the planned image and the real-time image. Then, the motion trajectory of the reference object can be determined according to the boundary mask information and the deformation information, so as to facilitate the subsequent positioning and tracking of the tumor to be located according to the motion trajectory of the reference object. In this way, the present application does not need to initialize the polynomial coefficients through manual intervention, nor does it need to train a priori models, thereby improving the efficiency and accuracy of tumor localization.

[0035] The above tumor localization method can be applied to a tumor localization system. Figure 1 A schematic diagram of a scenario of a tumor localization system provided in an embodiment of the present disclosure. The tumor localization system may include: an image acquisition device 101 , a tumor localization device 102 and a storage server 103 .

[0036] The image acquisition device 101 is a device for acquiring tumor sites and surrounding normal tissues of a target object (e.g., a patient to be treated, an experimental subject, a phantom, etc.). In some embodiments, the image acquisition device 101 may be a cone-beam computed tomography (CBCT) device.

[0037] See also Figure 1 , the image acquisition device 101 may include a frame 1011, a tube 1012, a detector 1013 and a support device 1014. The frame 1011 may be a rotatable frame. The tube 1012 may emit imaging rays, such as kilovolt (KV) level X-rays. The detector 1013 may be a detection plate facing the tube 1012, such as an X-ray detection plate. The support device 1014 is used to support and move the target object, and may be a treatment bed.

[0038] In some embodiments, when the target object is on the support device 1014, the rotation of the rack 1011 can drive the tube 1012 to perform 360-degree projection around the target object. After the imaging ray passes through the target object, it can be projected onto the detector 1013. At this time, the detector 1013 can collect the projection data after the projection of the tube 1012. Then, after collecting the projection data of multiple projections, a medical image (such as a CBCT image) of the target object can be obtained through data reconstruction.

[0039] In the disclosed embodiment, the image acquisition device 101 is used to acquire a real-time image of a reference object in the target object, and then the real-time image can be uploaded to the tumor localization device 102 so that the tumor localization device 102 can perform a subsequent tumor localization method based on the real-time image.

[0040] The tumor localization device 102 is used to determine the motion trajectory of the reference object in the target object based on the real-time image captured by the image acquisition device 101 and the planned image obtained from the storage server 103, and then locate and track the tumor to be located in the target object based on the motion trajectory of the reference object.

[0041] In some embodiments, the tumor localization device 102 may be a computer device with a graphical user interface (GUI), and the computer device includes: one or more processors, a memory, and one or more applications. For example, the tumor localization device 102 may include a tumor localization system application, and the processor of the tumor localization device executes the tumor localization system application to implement the tumor localization method provided in the embodiments of the present application.

[0042] In the embodiment of the present disclosure, the entity of the tumor localization device 102 may be a terminal or a server with a display, which is not limited in the embodiment of the present application.

[0043] Optionally, the terminal may be at least one of a smart phone, a smart watch, a desktop computer, a laptop computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal, a laptop computer and the like.

[0044] Optionally, the above-mentioned server may be an image server. The image server is used to provide background services for the above-mentioned image acquisition device 101. In some embodiments, the image server may be an independent physical server, or a server cluster or distributed file system composed of multiple physical servers, or at least one of the cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data or artificial intelligence platforms, etc., which is not limited in the embodiments of the present disclosure. In some embodiments, the number of the above-mentioned image servers can be more or less, which is not limited in the embodiments of the present disclosure. Of course, the image server can also include other functions in order to provide more comprehensive and diversified services.

[0045] Furthermore, in some embodiments, the tumor localization device 102 may run a computer system, wherein the computer system includes a processor, and the processor is used to implement the tumor localization method provided in the embodiments of the present application.

[0046] Optionally, the tumor localization device 102 may also be connected to a radiotherapy device, so that the radiotherapy device may adjust the position of a target object on the radiotherapy device according to the motion trajectory of a reference object displayed by the tumor localization device 102 .

[0047] In some embodiments, the implementation environment of the tumor positioning method may also include a gamma knife treatment head, an accelerator treatment head or other radiotherapy heads, which may be arranged on a rack to emit therapeutic rays, such as gamma rays and MV-level X-rays. Thus, the implementation environment constitutes a radiotherapy system, and accordingly, the tumor positioning device 102 is also used to perform an image registration process based on the real-time image acquired by the image acquisition device 101, and then perform pre-treatment positioning adjustment or real-time image guidance during treatment on the target object based on the image registration result.

[0048] The storage server 103 is used to store the medical data of the target object, such as the case type of the target object, the radiotherapy plan (including the planned image of the reference object in the target object), the radiotherapy dose, etc. In the embodiment of the present disclosure, the storage server 103 can also be used to store the real-time image acquired by the above-mentioned image acquisition device 101, the motion trajectory of the reference object generated by the above-mentioned image server 102, etc. When the tumor localization device 102 executes the tumor localization method provided in the embodiment of the present application, the storage server 103 can send the planned image of the reference object in the target object to the tumor localization device 102, so that the tumor localization device 102 can execute the subsequent tumor localization method based on the planned image.

[0049] The following is based on Figure 1The tumor localization system shown is used to introduce the tumor localization method provided in the embodiment of the present disclosure.

[0050] The tumor localization method provided in the embodiment of the present disclosure is applied to Figure 1 The tumor localization device 102 in FIG. Figure 2 FIG. 2 is a flow chart of a tumor localization method provided in an embodiment of the present application. Figure 2 As shown, the tumor localization method includes: S201-S205.

[0051] S201. A tumor localization device acquires a planned image and a real-time image of a reference object associated with a motion trajectory of a tumor to be located.

[0052] As described in the background technology, if tumor localization is performed only based on the real-time image of the reference object, the efficiency and accuracy are low. In the embodiment of the present application, the tumor localization device can not only obtain the real-time image of the reference object, but also obtain the planned image of the reference object. In this way, by comparing the planned image and the real-time image, the motion trajectory of the reference object can be quickly and accurately determined, and then the tumor can be quickly and accurately located.

[0053] Optionally, the planning image may be a CT image. Compared with a two-dimensional KV projection image, a CT image is a three-dimensional image, which may be more conducive to the segmentation of a reference object (eg, the diaphragm or other tissues).

[0054] In some embodiments, the reference object includes at least one of the following: diaphragm, lung wall, lung apex. The motion trajectory of the reference object and the motion trajectory of the tumor to be located are usually strongly correlated, that is, they are associated. In this way, by determining the motion trajectory of the reference object that is associated with the motion trajectory of the tumor to be located, the positioning and tracking of the tumor to be located can be achieved.

[0055] S202: The tumor localization device determines boundary mask information of the reference object according to the planning image.

[0056] Specifically, when the reference object moves, it is usually its boundary that moves and changes, that is, the motion trajectory of the reference object is usually strongly correlated with the boundary of the reference object. For example, when the reference object is a diaphragm, the diaphragm moves with the human body's breathing. When a person inhales, the diaphragm contracts and the top of the diaphragm (that is, the upper boundary of the diaphragm) drops; when exhaling, the diaphragm relaxes and the top of the diaphragm rises. Therefore, after acquiring the planning image, the tumor localization device can determine the boundary mask information of the reference object based on the planning image, so as to facilitate the subsequent determination of the motion trajectory of the reference object.

[0057] S203: The tumor localization device determines deformation information of the reference object according to the planned image and the real-time image.

[0058] Specifically, during the treatment of the human body, the reference object may be deformed. When the reference object is deformed, its shape will also change accordingly. Therefore, by monitoring the position change and distance change of the reference object, the deformation of the reference object can be determined. Therefore, after acquiring the planned image and the real-time image, the tumor localization device can determine the deformation information of the reference object based on the planned image and the real-time image, so as to accurately determine the motion trajectory of the reference object in the subsequent process.

[0059] In some embodiments, the tumor localization device may compare the planned image with the real-time image to determine deformation information of the reference object.

[0060] Exemplarily, assuming that the planned image is a CT image and the real-time image is a KV image, since CT images are usually used to reconstruct three-dimensional images, the tumor localization device can obtain a digitally reconstructed radiograph (DRR) projection of the CT image.

[0061] Since KV images are usually based on a sequence of images at multiple angles, for each angle of the KV image, the tumor localization device can obtain the KV image and DRR projection of the angle, and determine the two-dimensional deformation field of the KV image and DRR projection of the angle, and then obtain the deformation field image corresponding to the two-dimensional deformation field. The deformation information of the reference object can be obtained through the deformation field image.

[0062] S204: The tumor localization device determines the motion trajectory of the reference object according to the boundary mask information and the deformation information.

[0063] Specifically, since the boundary mask information can clearly outline the outline of the reference object, the positions of multiple key feature points on the boundary of the reference object can be determined through the boundary mask information of the reference object. These feature points can be vertices on the boundary of the reference object, points with large curvature changes, etc.

[0064] When the reference object is deformed, the shape of its boundary mask will also change accordingly. Therefore, by monitoring the relative position changes and distance changes between the feature points on the boundary mask, the local deformation of the reference object can be inferred.

[0065] As time goes by, the above position update process is repeated. The tumor localization device can record the position of the reference object at each moment (determined by the boundary mask and deformation information) to form a position sequence. This position sequence can be used to construct the motion trajectory of the reference object.

[0066] Optionally, the tumor localization device may use curve fitting methods, such as polynomial fitting, spline fitting, etc., to connect these discrete position points into a continuous trajectory curve, thereby clearly showing the motion trajectory of the reference object.

[0067] Optionally, the tumor localization device can also collect other auxiliary information of the reference object, such as the texture features inside the reference object, the relative position relationship with the surrounding tissues, etc. For example, the change direction of the texture inside the reference object can assist the tumor localization device in determining the movement direction of the reference object, the relative displacement between the reference object and the surrounding fixed tissues, etc., so as to further correct the movement trajectory and improve the accuracy of the movement trajectory determination.

[0068] S205 . The tumor localization device tracks the tumor to be located according to the motion trajectory of the reference object.

[0069] Specifically, when locating and tracking the tumor to be located according to the motion trajectory of the reference object, the tumor localization device can first establish the relative position relationship between the reference object and the tumor to be located. For example, the tumor localization device can determine the relative position of the reference object and the tumor to be located through medical images (such as CT, MRI, etc.). The tumor localization device can achieve the above-mentioned determination of the relative position by marking the center coordinates, boundary range and other information of the reference object and the tumor to be located on the image.

[0070] Next, the tumor localization device can determine a fixed geometric relationship between the reference object and the tumor to be located, for example, whether the reference object and the tumor to be located are connected, contained, or separated by a certain angle and distance.

[0071] After determining the motion trajectory of the reference object, the tumor localization device can convert the motion trajectory of the reference object into a potential motion trajectory of the tumor to be located according to the relative position relationship established previously.

[0072] Optionally, the tumor localization device can also use coordinate transformation and geometric translation, rotation and other operations to achieve the conversion of the motion trajectory of the reference object and the potential motion trajectory of the tumor to be located. For example, if the motion trajectory of the reference object is represented by a series of coordinate points "(X, Y, Z)" in a Cartesian coordinate system, and the position offset of the tumor to be located relative to the reference object is "(△a, △b, △c)", then the motion trajectory points of the tumor to be located can be expressed as (X+△a, Y+△b, Z+△c).

[0073] In some embodiments, since the physiological movements of the human body (such as breathing, heartbeat, etc.) will have a periodic impact on the position of the tumor to be located and the reference object, the tumor localization device can also analyze the laws of these physiological movements, separate or integrate them from the motion trajectory of the reference object, so as to accurately determine the motion trajectory of the reference object.

[0074] For example, assuming that the motion trajectory of the reference object includes the displacement caused by respiratory movement, and the movement of the tumor to be located has a certain correlation with the respiratory movement (such as synchronous movement), the tumor localization device can analyze the respiratory signal (such as obtained through the respiratory belt) and determine the position of the tumor to be located according to the different stages of the respiratory cycle, thereby improving the accuracy of positioning and tracking.

[0075] In some embodiments, in combination Figure 2 ,like Figure 3 As shown, in the above S202, the method in which the tumor localization device determines the boundary mask information of the reference object according to the planning image specifically includes:

[0076] S301. A tumor localization device determines a three-dimensional mask of a reference object according to a planning image.

[0077] Specifically, in order to accurately determine the boundary of the reference object and further determine the motion trajectory of the reference object, the tumor localization device may first determine a three-dimensional mask of the reference object according to the planning image.

[0078] In some embodiments, the planning image may include multiple layers of planning sub-images of the reference object continuously acquired along a preset anatomical direction. In this case, the method for the tumor localization device to determine the three-dimensional mask of the reference object based on the planning image specifically includes: for each layer of the planning sub-image, the tumor localization device may determine the reference region to which the reference object belongs based on the planning sub-image, and perform polynomial fitting on the gradient information of the reference region in the planning sub-image to obtain the boundary information of the reference object in the planning sub-image. Subsequently, the tumor localization device may combine the boundary information of the reference object corresponding to each layer of the planning sub-image to obtain the three-dimensional mask of the reference object.

[0079] For example, Figure 4 As shown, taking the planning image as a CT image and the reference object as the diaphragm as an example, the preset anatomical direction can be the direction of the coronal plane. In this case, the planning image can include multiple coronal plane images (i.e., multi-layer planning sub-images). For each coronal plane image, the tumor localization device can obtain the reference area to which the diaphragm belongs in each coronal plane image, i.e., the lung cavity 401.

[0080] Optionally, the tumor localization device may directly load the human lung contour according to the coronal image to obtain the lung cavity 401 .

[0081] Optionally, the tumor localization device may also first load the outer contour mask1 of the human body according to the coronal image. Then the tumor localization device may determine the area less than the air threshold (such as -800HU (Houinck Unit)) in the coronal image as air mask2. Subsequently, the tumor localization device may determine the intersection of mask1 & mask2 as the lung cavity 401.

[0082] After obtaining the reference area to which the diaphragm belongs, namely the lung cavity 401, the tumor localization device can determine the bottom of the lung cavity as the initial range of the head and foot direction of the diaphragm, namely Figure 4 An initial range 402 of the diaphragm is shown.

[0083] Next, in the initial range 402 of the diaphragm, the tumor localization device can determine the gradient information in the head and foot direction, that is, to subtract adjacent pixels in the head and foot direction in the initial range 402 of the diaphragm (the upper pixel value in the head and foot direction minus the lower pixel value). If the result obtained by subtracting the upper pixel value from the lower pixel value is a positive number, the gradient value is positive. This means that in the head and foot direction, the pixel values ​​are decreasing, and the upper pixels are brighter than the lower pixels (assuming it is a grayscale image, the larger the pixel value, the brighter it is). On the contrary, if the result obtained by subtracting the upper pixel value from the lower pixel value is a negative number, the gradient value is negative. This means that in the head and foot direction, the pixel values ​​are increasing, and the upper pixels are darker than the lower pixels.

[0084] After obtaining the gradient information, the tumor localization device can determine the mask with a gradient value greater than a preset threshold as the boundary mask of the diaphragm, and perform polynomial fitting on the boundary mask of the diaphragm to obtain the diaphragm boundary (the left and right diaphragms), that is, the boundary information of the reference object.

[0085] Subsequently, the tumor localization device can combine the diaphragm boundaries corresponding to each coronal image to obtain a three-dimensional mask of the diaphragm.

[0086] Of course, the tumor localization device can also use artificial intelligence (AI) technology to determine the three-dimensional mask of the reference object.

[0087] S302: The tumor localization device converts the three-dimensional mask into a two-dimensional mask, and performs morphological reconstruction on the two-dimensional mask to obtain boundary mask information.

[0088] Specifically, after obtaining the three-dimensional mask of the reference object, since the boundary mask information of the reference object is in two-dimensional space, the tumor localization device can convert the three-dimensional mask into a two-dimensional mask and perform morphological reconstruction on the two-dimensional mask to obtain the boundary mask information.

[0089] Morphological reconstruction is an important operation in mathematical morphology. It is based on a mask image and aims to reconstruct a new image from a labeled image through morphological operations under the constraints of the mask image.

[0090] In the embodiment of the present application, since the planning image may have breathing artifacts (i.e., the position of the organ may be different at different scanning time points, thereby generating artifacts such as blur, ghosting or deformation on the image) during the process of the tumor localization device determining the three-dimensional mask of the reference object according to the planning image, the two-dimensional mask obtained by the three-dimensional mask conversion may have pseudo boundaries. In this case, the pseudo boundaries in the two-dimensional mask can be removed through morphological reconstruction to obtain accurate and clear boundary mask information.

[0091] In some embodiments, the method in which the tumor localization device performs morphological reconstruction on the two-dimensional mask to obtain boundary mask information specifically includes: the tumor localization device may perform corrosion processing and smoothing processing on the two-dimensional mask in sequence to obtain a processed two-dimensional mask, and determine the boundary mask of the reference object in the first direction according to the gradient information of the processed two-dimensional mask. Subsequently, the tumor localization device may perform expansion processing in the second direction of the reference object based on the boundary mask and the structural element of the reference object in the first direction to obtain the boundary mask information.

[0092] Continuing with the example of the diaphragm as the reference object, Figure 5 As shown, the tumor localization device may first perform an etching process, ie, an etching operation, on the two-dimensional mask of the diaphragm to obtain a two-dimensional mask 501 after the etching operation.

[0093] The purpose of the erosion operation is to eliminate the small protrusions in the two-dimensional mask, which are likely to be pseudo-boundaries due to breathing artifacts. By selecting a suitable structural element (such as a small circular or square structural element), the two-dimensional mask is eroded. The erosion operation shrinks the boundary of the mask inward, thereby removing those discontinuous and small pseudo-boundary parts.

[0094] Next, the tumor localization device may perform smoothing, i.e., smoothing (also referred to as open operation smoothing), on the two-dimensional mask after the erosion operation to obtain a smoothed two-dimensional mask 502. The smoothing operation may reduce discontinuous parts at the boundary of the two-dimensional mask, thereby making it smoother.

[0095] Next, the tumor localization device may determine a boundary mask 503 of the diaphragm in the first direction according to the processed gradient information of the two-dimensional mask.

[0096] Assuming that the first direction is the head-to-foot direction, the tumor localization device can use the pixel points with a gradient greater than 0 in the head-to-foot direction in the processed two-dimensional mask as boundary points, and connect and combine the boundary points to obtain a boundary mask of the diaphragm in the head-to-foot direction.

[0097] Subsequently, the tumor localization device may perform expansion processing based on the boundary mask of the diaphragm in the head-to-foot direction. The expanded structural element (ie, filter kernel) may be [0; 1; 1], and the direction of expansion may be from head to toe, thereby obtaining the boundary mask information 504 of the diaphragm.

[0098] In some embodiments, the boundary mask information includes multiple segments of sub-masks of the reference object. In this case, in combination with Figure 3 ,like Figure 6 As shown, in the above S204, the method in which the tumor localization device determines the motion trajectory of the reference object according to the boundary mask information and the deformation information specifically includes:

[0099] S601: For each sub-mask, the tumor localization device determines a deformation amount of the sub-mask according to the sub-mask and deformation information corresponding to the sub-mask.

[0100] Specifically, it can be seen from the above S203 that the tumor localization device can determine the deformation information of the reference object. For each sub-mask, the tumor localization device can obtain the deformation information corresponding to the sub-mask from the deformation information. It can be seen from the above S302 that the tumor localization device can obtain the boundary mask information of the reference object, and then determine each sub-mask from the boundary mask information. Subsequently, the tumor localization device can determine the deformation amount of the sub-mask according to the sub-mask and the deformation information corresponding to the sub-mask.

[0101] Exemplarily, assuming that the boundary mask information includes n sub-masks of the reference object, the deformation calculation formula d of the i-th sub-mask is i for:

[0102]

[0103] Among them, D(x,y) is the deformation information of the pixel point (x,y), and mask(x,y) is the sub-mask of the pixel point (x,y).

[0104] S602: The tumor localization device determines the motion trajectory of the reference object according to the deformation amount of each sub-mask segment.

[0105] Specifically, after determining the deformation amount of each sub-mask, the tumor localization device may perform deformation calculation on each sub-mask based on the deformation amount of each sub-mask, and generate a motion trajectory of the reference object through the sub-mask after the deformation calculation.

[0106] It should be noted that, since the above operation is performed on each image in the KV sequence image, the motion trajectory of the reference object is also a motion trajectory generated for one KV image. The tumor localization device can select the smoothest estimate from the motion trajectory corresponding to each KV image as the motion trajectory of the reference object, and determine the maximum range of its motion (e.g., the highest point, the lowest point, etc.) according to the motion trajectory of the reference object.

[0107] It can be seen from the above that by segmenting the boundary mask information, the deformation of each sub-mask segment can be accurately obtained, and then the motion trajectory of the reference object can be accurately determined, thereby improving the robustness of determining the motion trajectory of the reference object.

[0108] In some embodiments, in combination Figure 6 ,like Figure 7 As shown, in the above S205, the method in which the tumor localization device performs localization and tracking of the tumor to be located according to the motion trajectory of the reference object specifically includes:

[0109] S701. The tumor localization device obtains a motion trajectory mapping model.

[0110] The motion trajectory mapping model is used to represent the mapping relationship between the motion trajectory of the reference object and the motion trajectory of the tumor to be located.

[0111] Specifically, when generating the motion trajectory mapping model, it is necessary to collect data related to the motion trajectory, such as position data and motion data of the tumor to be located and the reference object.

[0112] Optionally, the position data of the tumor to be located can be obtained regularly by medical imaging technology (such as CT, MRI or PET-CT). These imaging devices can provide the coordinates of the tumor to be located in three-dimensional space, and the time interval between each scan should remain relatively stable, such as once a week or every two weeks.

[0113] The motion data of the reference object can also be obtained regularly using medical imaging. During the breathing process, the position and shape of the reference object (such as the diaphragm) will change. The embodiments of the present application can use methods such as dynamic MRI or fluoroscopy to record the motion trajectory of the reference object during the respiratory cycle and obtain its position information at different respiratory phases.

[0114] After obtaining the above data related to the motion trajectory, these data may be preprocessed to obtain processed trajectory data.

[0115] The preprocessing may include coordinate system alignment processing and time synchronization processing.

[0116] Since the position of the tumor to be located and the motion data of the reference object may come from different imaging devices or scanning sequences, their coordinate systems need to be aligned, for example, geometric transformations such as translation, rotation, and scaling to ensure that all data are in a unified spatial coordinate system.

[0117] Furthermore, since the position of the tumor to be located and the movement of the reference object are processes that change over time, the data must be synchronized in time to determine the time points of each scan of the tumor to be located and the movement record of the reference object, and to arrange them in chronological order so that an accurate mapping relationship can be established later.

[0118] Next, the tumor position features and reference object motion features in the processed trajectory data may be extracted to establish a motion trajectory mapping model.

[0119] Tumor location features may include dynamic features such as three-dimensional coordinates, tumor location change speed, acceleration, etc. These features can help describe the motion state of the tumor to be located at different time points, for example, by calculating the difference between two adjacent tumor scans to obtain the location change speed.

[0120] The reference object motion characteristics may include the rise and fall amplitude, motion speed, respiratory frequency, etc. of the reference object in the respiratory cycle. For example, taking the diaphragm as the reference object, the motion amplitude of the diaphragm is determined by analyzing the position difference of the diaphragm at the peak of inspiration and expiration.

[0121] Optionally, when establishing a motion trajectory mapping model, if there is an approximately linear relationship between the position of the tumor to be located and the motion of the reference object, a linear regression model can be considered. If the relationship between the position of the tumor to be located and the motion of the reference object is more complex, a nonlinear model may need to be used. For example, a simple multi-layer perceptron (MLP) neural network can be constructed. The input layer of the neural network receives the motion features of the reference object, the hidden layer performs nonlinear transformation, and the output layer outputs the tumor position features. The neural network is trained with a large amount of data, and the weights and biases of the network are adjusted to obtain an accurate motion trajectory mapping model.

[0122] S702: The tumor localization device tracks the tumor to be located according to the motion trajectory of the reference object and the motion trajectory mapping model.

[0123] Optionally, when there are multiple reference objects, the motion trajectory mapping model can also be a mapping model of the motion trajectories of multiple reference objects and the motion trajectory of the tumor to be located. In other words, the tumor localization device can locate and track the tumor to be located based on the motion trajectories of multiple reference objects and the motion trajectory mapping model.

[0124] In some embodiments, the motion trajectory of the reference object includes the actual motion amount of the reference object at each motion point. Figure 6 ,like Figure 8 As shown, in the above S205, the method in which the tumor localization device performs localization and tracking of the tumor to be located according to the motion trajectory of the reference object specifically includes:

[0125] S801. The tumor positioning device obtains the normal movement amount of the reference object.

[0126] For example, taking the diaphragm as the reference object, the movement of the diaphragm usually changes reasonably according to the breathing of the human body. In this case, the tumor localization device can obtain the normal movement amount of the diaphragm.

[0127] S802: The tumor localization device tracks the tumor to be located according to the actual movement amount and the normal movement amount of the reference object at each movement point.

[0128] Continuing with the above example, if the actual movement amount of the diaphragm at a certain movement point (such as the amplitude of the rise) exceeds the normal movement amount of the diaphragm, it means that the patient's breathing is abnormal.

[0129] For another example, the tumor localization device can set a reference baseline based on the normal movement of the diaphragm at each movement point. If the actual movement of the patient's diaphragm exceeds the baseline range (i.e., baseline drift), it may be caused by the patient's gradual relaxation as the treatment progresses.

[0130] In the above two cases, the tumor localization device can remind the doctor to check the patient's target area by outputting prompt information.

[0131] Optionally, when there are multiple reference objects, the tumor localization device can also obtain the normal motion ranges of the multiple reference objects. In the process of localizing and tracking the tumor to be located, the tumor localization device can obtain the actual motion ranges of the multiple reference objects, and compare them with the normal motion ranges of the multiple reference objects to determine whether the actual motion ranges of each reference object are abnormal, and then determine whether the patient's breathing state is abnormal.

[0132] When the tumor localization device compares the actual motion range of multiple reference objects with the normal motion range of multiple reference objects, it can subtract the normal motion value from the actual motion value of the reference object and determine whether the difference exceeds the preset range. Subsequently, the difference is mapped to a risk coefficient and the weighted average of the risk coefficients of all reference objects is used to obtain the final risk coefficient. By determining whether the final risk coefficient exceeds the preset threshold, it can be determined whether the patient's breathing state is abnormal at this time.

[0133] The above mainly introduces the scheme of the embodiment of the present application from the perspective of method. It is understandable that in order to realize the above functions, the tumor localization device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiments of the present application.

[0134] The embodiment of the present application can divide the tumor localization device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0135] like Fig. 9 As shown, the embodiment of the present application provides a tumor localization device, including: a communication unit 901 and a processing unit 902;

[0136] A communication unit 901 is used to obtain a planned image and a real-time image of a reference object associated with the motion trajectory of the tumor to be located;

[0137] A processing unit 902 is used to determine the boundary mask information of the reference object according to the planning image;

[0138] The processing unit 902 is further used to determine deformation information of the reference object according to the planned image and the real-time image;

[0139] The processing unit 902 is further used to determine the motion trajectory of the reference object according to the boundary mask information and the deformation information;

[0140] The processing unit 902 is further configured to track the tumor to be located according to the motion trajectory of the reference object.

[0141] In some embodiments, the processing unit 902 is specifically configured to:

[0142] determining a three-dimensional mask of the reference object based on the planning image;

[0143] The 3D mask is converted into a 2D mask, and the 2D mask is morphologically reconstructed to obtain the boundary mask information.

[0144] In some embodiments, the planning image includes multi-layer planning sub-images of the reference object continuously acquired along a preset anatomical direction; the processing unit 902 is specifically configured to:

[0145] For each layer of planned sub-image, determining a reference region to which a reference object belongs according to the planned sub-image;

[0146] Performing polynomial fitting on the gradient information of the reference area in the planned sub-image to obtain the boundary information of the reference object in the planned sub-image;

[0147] The boundary information of the reference object corresponding to each layer of the planned sub-image is combined to obtain a three-dimensional mask of the reference object.

[0148] In some embodiments, the processing unit 902 is specifically configured to:

[0149] performing an erosion process and a smoothing process on the two-dimensional mask in sequence to obtain a processed two-dimensional mask;

[0150] Determining a boundary mask of the reference object in a first direction according to the processed gradient information of the two-dimensional mask;

[0151] Based on the boundary mask and the structure element of the reference object in the first direction, an expansion process is performed toward the second direction of the reference object to obtain boundary mask information.

[0152] In some embodiments, the boundary mask information includes a plurality of sub-masks of the reference object; the processing unit 902 is specifically configured to:

[0153] For each sub-mask segment, determine the deformation amount of the sub-mask according to the sub-mask and the deformation information corresponding to the sub-mask;

[0154] According to the deformation amount of each sub-mask segment, the motion trajectory of the reference object is determined.

[0155] In some embodiments, the processing unit 902 is specifically configured to:

[0156] Acquire a motion trajectory mapping model; the motion trajectory mapping model is used to represent a mapping relationship between a motion trajectory of a reference object and a motion trajectory of a tumor to be located;

[0157] The tumor to be located is tracked according to the motion trajectory of the reference object and the motion trajectory mapping model.

[0158] In some embodiments, the motion trajectory of the reference object includes the actual motion amount of the reference object at each motion point; the processing unit 902 is specifically configured to:

[0159] Obtaining the normal amount of motion of a reference object;

[0160] The tumor to be located is tracked according to the actual movement amount and normal movement amount of the reference object at each movement point.

[0161] In some embodiments, the reference object includes at least one of the following: diaphragm, lung wall, lung apex.

[0162] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the tumor localization method provided by the present disclosure.

[0163] According to an embodiment of the present disclosure, the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable an electronic device to execute the tumor localization method provided by the present disclosure.

[0164] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, including a computer program, which implements the tumor localization method provided by the present disclosure when executed by a processor.

[0165] Fig.10 A schematic block diagram of an example electronic device 1000 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein. In some embodiments, the electronic device may be the above-mentioned Figure 1 The tumor localization device shown in .

[0166] like Fig.10As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 to a random access memory (RAM) 1003. In the random access memory 1003, various programs and data required for the operation of the electronic device 1000 can also be stored. The computing unit 1001, the read-only memory 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0167] Multiple components in the electronic device 1000 are connected to the input / output interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0168] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors, and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as a data matching method. For example, in one embodiment, the data matching method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1008. In one embodiment, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via ROM 1002 and / or a communication unit 1009. When the computer program is loaded into RAM 1003 and executed by the computing unit 1001, one or more steps of the data matching method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to perform the data matching method in any other appropriate manner (eg, by means of firmware).

[0169] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays, application specific integrated circuits, application specific standard parts (ASSP), system on chip systems (System On Chip, SOC), complex programmable logic devices (Complex Programmable Logic Device, CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0170] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0171] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical fibers, portable compact disk read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0172] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user, such as a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0173] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

[0174] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0175] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0176] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A tumor localization method, characterized in that: include: Acquire a planned image and a real-time image of a reference object associated with the motion trajectory of the tumor to be located; Determining boundary mask information of the reference object according to the planning image; determining deformation information of the reference object according to the planned image and the real-time image; determining a motion trajectory of the reference object according to the boundary mask information and the deformation information; The tumor to be located is located and tracked according to the motion trajectory of the reference object.

2. The method according to claim 1, characterized in that The step of determining the boundary mask information of the reference object according to the planned image comprises: determining a three-dimensional mask of the reference object based on the planning image; The three-dimensional mask is converted into a two-dimensional mask, and the two-dimensional mask is morphologically reconstructed to obtain the boundary mask information.

3. The method according to claim 2, characterized in that The planning image includes multi-layer planning sub-images of the reference object continuously acquired along a preset anatomical direction; Determining the three-dimensional mask of the reference object according to the planning image comprises: For each layer of planned sub-image, determining a reference region to which the reference object belongs according to the planned sub-image; Performing polynomial fitting on the gradient information of the reference area in the planned sub-image to obtain the boundary information of the reference object in the planned sub-image; The boundary information of the reference object corresponding to each layer of the planned sub-image is combined to obtain a three-dimensional mask of the reference object.

4. The method according to claim 2, characterized in that: The morphological reconstruction of the two-dimensional mask to obtain the boundary mask information includes: performing an erosion process and a smoothing process on the two-dimensional mask in sequence to obtain a processed two-dimensional mask; determining a boundary mask of the reference object in a first direction according to the gradient information of the processed two-dimensional mask; Based on the boundary mask and the structure element of the reference object in the first direction, an expansion process is performed in a second direction of the reference object to obtain the boundary mask information.

5. The method according to claim 1, characterized in that The boundary mask information includes a plurality of sub-masks of the reference object; and determining the motion trajectory of the reference object according to the boundary mask information and the deformation information includes: For each sub-mask, determining a deformation amount of the sub-mask according to the sub-mask and deformation information corresponding to the sub-mask; The motion trajectory of the reference object is determined according to the deformation amount of each sub-mask segment.

6. The method according to claim 1, characterized in that The step of tracking the tumor to be located according to the motion trajectory of the reference object includes: Acquire a motion trajectory mapping model; the motion trajectory mapping model is used to represent a mapping relationship between the motion trajectory of the reference object and the motion trajectory of the tumor to be located; The tumor to be located is located and tracked according to the motion trajectory of the reference object and the motion trajectory mapping model.

7. The method according to claim 1, characterized in that The motion trajectory of the reference object includes the actual motion amount of the reference object at each motion point; and positioning and tracking the tumor to be located according to the motion trajectory of the reference object includes: Acquiring a normal amount of motion of the reference object; The tumor to be located is located and tracked according to the actual movement amount of the reference object at each movement point and the normal movement amount.

8. The method according to any one of claims 1 to 7, characterized in that: The reference object includes at least one of the following: diaphragm, lung wall, and lung apex.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory configured to store instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1-8.

10. A non-volatile storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is read and executed, the method described in any one of claims 1 to 8 is implemented.

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