Model training method, tumor segmentation method and electronic device

Abstract

Embodiments of the present invention disclose a model training method, a tumor segmentation method, and an electronic device. The model training method may include: obtaining an original medical image containing a tumor; determining a gradient map of the original medical image, obtaining seed points based on local maxima in the gradient map, and using the seed points to segment the original medical image to obtain at least two regions; generating enhancement coefficients for each region to obtain a sub-region enhancement map, and performing image enhancement on the original medical image based on the sub-region enhancement map to obtain an enhanced medical image; and training an initial segmentation model based on the original medical image and the enhanced medical image to obtain a tumor segmentation model. The technical solution of the embodiments of the present invention solves the problem of being unable to accurately segment tumors from medical images.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of medical image processing technology, and in particular to a model training method, a tumor segmentation method, and an electronic device. Background Art

[0002] Deep learning-based medical image segmentation models continue to advance, often based on the independent and identically distributed (IID) assumption, which states that training and test images are independently and identically distributed. However, in clinical practice, variability in imaging equipment, protocols, and parameters, and even patient radiation exposure and imaging duration, can lead to differences in image distributions, a major obstacle hindering the application of these models in clinical practice.

[0003] To address these obstacles, researchers have conducted research on single-source domain generalization. Single-source domain generalization aims to train robust models based on a single source domain image, enabling direct application to unseen target domain images. To address domain distribution discrepancies and improve model robustness, one approach is to generate new images by augmenting source domain images, thereby simulating images from different domains and enriching the training set.

[0004] However, the model trained based on the currently used image enhancement scheme can be applied to the segmentation of tissues or organs with relatively obvious boundaries, but is not suitable for segmenting tumors with unclear boundaries. In other words, it is impossible to accurately segment tumors from medical images, which urgently needs to be solved. Summary of the Invention

[0005] The embodiments of the present invention provide a model training method, a tumor segmentation method, and an electronic device, which solve the problem of being unable to accurately segment tumors from medical images.

[0006] According to one aspect of the present invention, a model training method is provided, which may include:

[0007] Acquire original medical images containing tumors;

[0008] Determining a gradient map of the original medical image, obtaining a seed point based on a local maximum in the gradient map, and segmenting the original medical image using the seed point to obtain at least two regions;

[0009] Generating an enhancement coefficient for each region to obtain a sub-region enhancement map, and performing image enhancement on the original medical image according to the sub-region enhancement map to obtain an enhanced medical image;

[0010] Based on the original medical image and the enhanced medical image, an initial segmentation model is trained to obtain a tumor segmentation model.

[0011] According to another aspect of the present invention, a tumor segmentation method is provided, which may include:

[0012] Acquire a target medical image containing a tumor and a tumor segmentation model trained according to the model training method described in any embodiment of the present invention;

[0013] The target medical image is input into the tumor segmentation model, and the target segmentation result of the target medical image for the tumor is obtained according to the output result of the tumor segmentation model.

[0014] According to another aspect of the present invention, there is provided an electronic device, which may include:

[0015] at least one processor; and

[0016] a memory communicatively connected to at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor implements the model training method or tumor segmentation method provided by any embodiment of the present invention when executing the computer program.

[0018] The technical solution of the embodiment of the present invention obtains an original medical image containing a tumor; determines a gradient map of the original medical image, and then uses the local maximum value in the gradient map as a seed point to segment the original medical image using the seed point to obtain at least two regions, thereby implementing a regional division process based on anatomical information; further, generates an enhancement coefficient for each region to obtain a regional enhancement map, and then performs image enhancement on the original medical image based on the regional enhancement map to obtain an enhanced medical image, thereby implementing image enhancement for each region, that is, image enhancement based on anatomical information; thus, an initial segmentation model can be trained based on the original medical image and the enhanced medical image to obtain a tumor segmentation model. The above technical solution performs a regional image enhancement process based on anatomical information during the model training process. This process divides the original medical image into different regions based on different tissues and / or organs in the anatomical information, and then performs different image enhancement on each region at the regional level. This allows the image enhancement to preserve the intensity difference between the tumor and objects other than the tumor within the tissue and / or organ where the tumor is located, thereby allowing the model to utilize the intensity difference feature to improve its segmentation performance, that is, to train a tumor segmentation model suitable for tumor segmentation, achieving accurate tumor segmentation.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flowchart of a model training method provided according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of an example of regional image enhancement based on anatomical information in a model training method provided according to an embodiment of the present invention;

[0023] Figure 3 is a flowchart of another model training method provided according to an embodiment of the present invention;

[0024] Figure 4 is a flowchart of another model training method provided according to an embodiment of the present invention;

[0025] Figure 5 is a flow chart of a tumor segmentation method provided according to an embodiment of the present invention;

[0026] Figure 6 is a schematic diagram of an example of single-source domain generalization in a tumor segmentation method provided according to an embodiment of the present invention;

[0027] Figure 7 is a structural block diagram of a model training device provided according to an embodiment of the present invention;

[0028] Figure 8 is a structural block diagram of a tumor segmentation device provided according to an embodiment of the present invention;

[0029] Figure 9 It is a structural diagram of an electronic device for implementing the model training method or tumor segmentation method of an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The situations of "target", "original", etc. are similar and will not be repeated here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Before introducing the embodiments of the present invention, an example application scenario of the embodiments of the present invention is first described. For example, in medical images, the intensity difference between a tumor and its surrounding normal area is an important feature for determining the tumor boundary. This intensity difference, as part of the image texture, is often destroyed in current image enhancement schemes along with the destruction of the image texture, making it difficult for the model to extract sufficient feature information to determine the tumor boundary, and thus making the trained model unable to accurately segment the tumor.

[0033] Figure 1 This is a flow chart of a model training method provided in an embodiment of the present invention. This embodiment is applicable to training a tumor segmentation model suitable for segmenting tumors. This method can be performed by a model training device provided in an embodiment of the present invention. This device can be implemented in software and / or hardware and can be integrated into an electronic device, such as various user terminals or servers.

[0034] See also Figure 1 The method of the embodiment of the present invention specifically includes the following steps:

[0035] S110. Acquire an original medical image containing a tumor.

[0036] The original medical image can be understood as a medical image obtained by capturing an image of the tumor site, i.e., a medical image containing the tumor. The image type of the medical image can be, for example, magnetic resonance imaging (MR) or computed tomography (CT), etc. This depends on the actual situation and is not specifically limited here. The original medical image can be obtained, in particular, by obtaining multiple original medical images so that the multiple original medical images can be used for model training.

[0037] After obtaining the multiple original medical images, in practical applications, the multiple original medical images can be optionally preprocessed, for example, the directions and voxels of the multiple original medical images can be unified, and maximum and minimum normalization can be performed to normalize the multiple original medical images within the range of [-1,1], so that the multiple original medical images have the same attributes, thereby improving the model training effect.

[0038] S120. Determine a gradient map of the original medical image, obtain seed points based on local maxima in the gradient map, and use the seed points to segment the original medical image to obtain at least two regions.

[0039] For each acquired original medical image, S120 and S130 are executed as follows:

[0040] A gradient map of the original medical image is determined. This gradient map can reflect the rate of change of the pixel value of each pixel in the original medical image in the spatial domain. This helps identify pixels located on region boundaries among all pixels in the original medical image. Furthermore, a local maximum is determined. This local maximum can be understood as the pixel value with the largest value within a local region of the gradient map. The pixel corresponding to this pixel value is likely located on a region boundary. Therefore, a seed point can be derived based on this local maximum, for example, using the pixel corresponding to the local maximum as the seed point. Further, the seed point can be used to segment the original medical image to obtain two or more regions within the original medical image.

[0041] It should be noted that the original medical image contains one or more objects, and the tumor is located in the one or more objects, which can be tissues and / or organs. On this basis, since the divided regions are the regions where the one or more objects are located, the above-mentioned region division process (i.e., the original medical image segmentation process) can be understood as a region division process based on anatomical information.

[0042] S130. Generate an enhancement coefficient for each region to obtain a sub-region enhancement map, and perform image enhancement on the original medical image according to the sub-region enhancement map to obtain an enhanced medical image.

[0043] The enhancement coefficient can be understood as a coefficient used to enhance the image of a certain divided area. This coefficient can represent the enhancement direction and / or enhancement magnitude, etc. An enhancement coefficient is randomly generated for each area to obtain a sub-region enhancement map. This sub-region enhancement map can be understood as an image representing each enhancement coefficient. In practical applications, optionally, a set of enhancement coefficients can be generated, that is, an enhancement coefficient is generated for each area to obtain a sub-region enhancement map; multiple sets of enhancement coefficients can also be generated, and a sub-region enhancement map can be obtained based on each set of enhancement coefficients; and so on. This can be set according to actual needs and is not limited here.

[0044] Furthermore, for each sub-region enhancement map, the original medical image is enhanced according to the sub-region enhancement map to obtain an enhanced medical image corresponding to the sub-region enhancement map. In practical applications, the product of the sub-region enhancement map and the original medical image, particularly the Hadamard product, can optionally be used as the corresponding enhanced medical image. Thus, image enhancement of the original medical image is completed.

[0045] It should be noted that, according to the above explanation, this step generates an enhancement coefficient for each region separately, and performs image enhancement of the original medical image based on the regional enhancement map obtained thereby. This is equivalent to enhancing each region in the original medical image separately, that is, performing image enhancement on a single region as a whole, rather than performing image enhancement on the original medical image as a whole.

[0046] In this way, for the target area in each region where the tissue and / or organ containing the tumor is located, the image of the target area can be enhanced separately, which helps to retain the intensity difference between the intensity information of the tumor in the target area and the intensity information of the objects other than the tumor in the target area, that is, to retain the intensity difference between normal tissue and / or organ and the tumor inside the tissue and / or organ, so that the model can determine the boundary of the tumor based on the feature of the intensity difference, and thus the trained model can accurately segment the tumor.

[0047] S140. Based on the original medical image and the enhanced medical image, train an initial segmentation model to obtain a tumor segmentation model.

[0048] Original medical images can be understood as medical images in the source domain, and enhanced medical images can be understood as medical images in a domain other than the source domain. The initial segmentation model is trained based on multiple original medical images and one or more enhanced medical images corresponding to each original medical image. This allows the initial segmentation model to learn medical images from different domains, ensuring the segmentation performance of the trained tumor segmentation model.

[0049] The technical solution of the embodiment of the present invention obtains an original medical image containing a tumor; determines a gradient map of the original medical image, and then uses the local maximum value in the gradient map as a seed point to segment the original medical image using the seed point to obtain at least two regions, thereby implementing a regional division process based on anatomical information; further, generates an enhancement coefficient for each region to obtain a regional enhancement map, and then performs image enhancement on the original medical image based on the regional enhancement map to obtain an enhanced medical image, thereby implementing image enhancement for each region, that is, image enhancement based on anatomical information; thus, an initial segmentation model can be trained based on the original medical image and the enhanced medical image to obtain a tumor segmentation model. The above technical solution performs a regional image enhancement process based on anatomical information during the model training process. This process divides the original medical image into different regions based on different tissues and / or organs in the anatomical information, and then performs different image enhancement on each region at the regional level. This allows the image enhancement to preserve the intensity difference between the tumor and objects other than the tumor within the tissue and / or organ where the tumor is located, thereby allowing the model to utilize the intensity difference feature to improve its segmentation performance, that is, to train a tumor segmentation model suitable for tumor segmentation, achieving accurate tumor segmentation.

[0050] An optional technical solution, using seed points to segment the original medical image to obtain at least two regions, may include: using the seed points to apply a preset watershed algorithm on the original medical image to segment the original medical image into at least two regions.

[0051] The watershed algorithm performs image segmentation based on the composition of a watershed, making it highly applicable to medical image segmentation. This technical solution, combining gradient maps with the watershed algorithm, extracts anatomical information from the original medical image. This accurate extraction of anatomical information ensures accurate delineation of regions within the original medical image.

[0052] In order to better understand the above technical solutions, the following is an illustrative description with reference to specific examples. Figure 2 , first calculate the gradient map of the original hospital image, then select the pixel point corresponding to the local maximum value of the gradient map as the seed point, and use the seed point to use the watershed algorithm to divide different regions on the original medical image, thereby realizing the division of different tissues and / or organs into different regions based on anatomical information. Further, an enhancement coefficient is randomly generated in each region, and the enhancement coefficient can also be called a weighting coefficient, so as to obtain a regional enhancement map. It should be noted that different enhancement coefficients are represented by different colors in the regional enhancement map, that is, different regions correspond to different colors in the regional enhancement map. Further, the regional enhancement map is Hadamard-producted with the original medical image to obtain an enhanced medical image. In Figure 2In the example shown, two sets of enhancement coefficients are generated, thereby obtaining two region-specific enhancement maps and further obtaining two enhanced medical images.

[0053] In the above example, while performing image enhancement, the intensity difference between the tumor and objects other than the tumor in the tissue and / or organ where the tumor is located is effectively preserved.

[0054] Figure 3 It is a flowchart of another model training method provided in an embodiment of the present invention. This embodiment is optimized based on the above-mentioned technical solutions. In this embodiment, optionally, after obtaining the original medical image containing the tumor, the above-mentioned model training method also includes: for the selection probabilities preset for at least two image enhancement strategies, selecting a target enhancement strategy from at least two image enhancement strategies according to each selection probability; accordingly, determining the gradient map of the original medical image, including: when the target enhancement strategy is a regional enhancement strategy, determining the gradient map of the original medical image. Among them, the explanations of the terms that are the same as or corresponding to the above-mentioned embodiments are not repeated here.

[0055] See also Figure 3 The method of this embodiment may specifically include the following steps:

[0056] S210. Acquire an original medical image containing a tumor.

[0057] S220. For the selection probabilities respectively preset for the at least two image enhancement strategies, select a target enhancement strategy from the at least two image enhancement strategies according to each selection probability.

[0058] Among them, the image enhancement strategy can be understood as a pre-set strategy for image enhancement, such as the regional enhancement strategy described above, or a global enhancement strategy, etc., which can be set according to actual needs and is not specifically limited here. It should be noted that the regional enhancement strategy is an image enhancement at the regional level, while the global enhancement strategy is an image enhancement at the image level, that is, the two are image enhancements of different scales. A selection probability is preset for each image enhancement strategy, and the selection probability can be understood as the probability of selecting the corresponding image enhancement strategy when performing image enhancement for a certain original medical image. In actual applications, optionally, considering that the regional enhancement strategy helps to preserve intensity differences compared to the global enhancement strategy, a larger selection probability can be preset for the regional enhancement strategy compared to the global enhancement strategy.

[0059] After the original medical image is acquired, a target enhancement strategy may be selected from at least two image enhancement strategies according to each selection probability, so as to perform image enhancement on the original medical image using the target enhancement strategy.

[0060] S230. When the target enhancement strategy includes a region enhancement strategy, determine the gradient map of the original medical image, obtain seed points based on the local maximum value in the gradient map, and use the seed points to segment the original medical image to obtain at least two regions.

[0061] Wherein, when the target enhancement strategy is a region enhancement strategy, S230 and S240 may be used to divide the original medical image into regions, and image enhancement may be performed separately on each divided region.

[0062] S240. Generate an enhancement coefficient for each region to obtain a sub-region enhancement map, and perform image enhancement on the original medical image according to the sub-region enhancement map to obtain an enhanced medical image.

[0063] S250. Based on the original medical image and the enhanced medical image, train an initial segmentation model to obtain a tumor segmentation model.

[0064] The technical solution of the embodiment of the present invention presets image enhancement strategies of different scales and selects one from each image enhancement strategy for image enhancement based on the selection probability preset for each image enhancement strategy. The application of the image enhancement strategies of different scales thereby improves the diversity of the texture (i.e., style) of the enhanced medical image.

[0065] In an optional technical solution, after selecting the target enhancement strategy from at least two image enhancement strategies according to each selection probability, the above-mentioned model training method further includes:

[0066] When the target enhancement strategy is a global enhancement strategy, the original medical image is enhanced as a whole to obtain an enhanced medical image.

[0067] Exemplarily, here we take the global enhancement strategy based on Bezier curve as an example, and use the cubic Bezier curve to perform a global enhancement transformation on the original medical image. The starting point and end point are set to (-1, -1) and (1, 1), or (-1, 1) and (1, -1), respectively, and the remaining two control points are randomly selected in the range of (-1, 1). In this way, an enhanced medical image with a similar intensity distribution to the original medical image and / or an enhanced medical image with an opposite intensity distribution can be obtained, thereby achieving global enhancement of the original medical image.

[0068] Figure 4It is a flowchart of another model training method provided in an embodiment of the present invention. This embodiment is optimized based on the above-mentioned technical solutions. In this embodiment, optionally, an initial segmentation model is trained based on the original medical image and the enhanced medical image to obtain a tumor segmentation model, including: inputting the original medical image into the initial segmentation model to obtain the original segmentation result, and inputting the enhanced medical image into the initial segmentation model to obtain the enhanced segmentation result; calculating the consistency loss according to the original segmentation result and the enhanced segmentation result, and adjusting the parameters in the initial segmentation model according to the consistency loss to obtain the tumor segmentation model. Among them, the explanations of the terms that are the same as or corresponding to the above-mentioned embodiments are not repeated here.

[0069] See also Figure 4 The method of this embodiment may specifically include the following steps:

[0070] S310. Acquire an original medical image containing a tumor.

[0071] S320. Determine a gradient map of the original medical image, obtain seed points based on local maxima in the gradient map, and use the seed points to segment the original medical image to obtain at least two regions.

[0072] S330. Generate an enhancement coefficient for each region to obtain a sub-region enhancement map, and perform image enhancement on the original medical image according to the sub-region enhancement map to obtain an enhanced medical image.

[0073] S340. Input the original medical image into the initial segmentation model to obtain the original segmentation result, and input the enhanced medical image into the initial segmentation model to obtain the enhanced segmentation result.

[0074] S350. Calculate the consistency loss based on the original segmentation result and the enhanced segmentation result, and adjust the parameters in the initial segmentation model based on the consistency loss to obtain a tumor segmentation model.

[0075] Since the enhanced medical image is obtained by performing image enhancement on the original medical image, the segmentation result corresponding to the enhanced medical image (i.e., the enhanced segmentation result) should be consistent with the segmentation result corresponding to the original medical image (i.e., the original segmentation result). On this basis, to improve the consistency between the segmentation results before and after enhancement, a consistency loss can be calculated based on the original segmentation result and the enhanced segmentation result. For example, the relative entropy between the two segmentation results can be calculated and then used as the consistency loss. Furthermore, the parameters of the initial segmentation model are adjusted based on this consistency loss, and model training is performed.

[0076] The technical solution of the embodiment of the present invention calculates the consistency loss between the original segmentation result and the enhanced segmentation result, and uses the consistency loss to train the model, thereby improving the consistency between the segmentation results before and after enhancement, and further improving the generalization of the model to medical images of different styles.

[0077] An optional technical solution, the above model training method further includes:

[0078] Obtain tumor segmentation labels for original medical images;

[0079] Calculate the original segmentation loss based on the segmentation label and the original segmentation result, and calculate the enhanced segmentation loss based on the segmentation label and the enhanced segmentation result;

[0080] The parameters in the initial segmentation model are adjusted according to the consistency loss to obtain a tumor segmentation model, including:

[0081] The training loss is obtained according to the original segmentation loss, the enhanced segmentation loss and the consistency loss, and the parameters in the initial segmentation model are adjusted according to the training loss to obtain the tumor segmentation model.

[0082] As explained above, since the enhanced medical image is obtained by performing image enhancement on the original medical image, the enhanced medical image and the original medical image correspond to the same segmentation label. Based on this, the original segmentation loss can be calculated based on the segmentation label and the original segmentation result, and the enhanced segmentation loss can be calculated based on the segmentation label and the enhanced segmentation result. The training loss (i.e., the total loss) is then calculated based on these two segmentation losses and the consistency loss, and the parameters are then adjusted based on this training loss.

[0083] The above technical solution further improves the model segmentation performance by introducing segmentation loss.

[0084] Figure 5 This is a flow chart of a tumor segmentation method provided by an embodiment of the present invention. This embodiment is applicable to tumor segmentation scenarios. This method can be performed by a tumor segmentation device provided by an embodiment of the present invention. This device can be implemented using software and / or hardware and can be integrated into an electronic device, such as various user terminals or servers.

[0085] See also Figure 5 The method of the embodiment of the present invention specifically includes the following steps:

[0086] S410. Obtain a target medical image containing a tumor, and a tumor segmentation model trained according to the model training method provided by any embodiment of the present invention.

[0087] S420. Input the target medical image into the tumor segmentation model, and obtain a target segmentation result of the target medical image for the tumor based on the output result of the tumor segmentation model.

[0088] The technical solution of the embodiment of the present invention realizes accurate segmentation of tumors in target medical images.

[0089] In order to better understand the above technical solutions as a whole, the following is an illustrative description of the above technical solutions with reference to specific examples. Figure 6 A single-source domain generalization scheme suitable for tumor segmentation is shown in FIG. The specific implementation process of the single-source domain generalization scheme is as follows:

[0090] Step 1: Arrange multiple original medical images, unify their directions and voxels, and perform maximum and minimum normalization to normalize the multiple original medical images to the range of [-1, 1].

[0091] Step 2: Perform image enhancement on the original medical image x through a multi-scale random enhancement module to obtain an enhanced medical image x ′ Specifically, in this example, the selection probability of the global enhancement strategy based on Bezier curve is preset to 0.2, and the selection probability of the regional enhancement strategy based on anatomical information is preset to 0.8. In each batch of training, the global enhancement strategy or the regional enhancement strategy is randomly selected according to the selection probability to perform image enhancement on the original medical image x, and the enhanced medical image x is obtained. ′ .

[0092] Step 3: Combine the original medical image x and the enhanced medical image x ′ All are input into the same initial segmentation model for training, enhancing the medical image x ′ The training is performed using the segmentation labels corresponding to the original medical image x. In this example, the initial segmentation model can be pre-constructed based on a segmentation network such as UNet.

[0093] Step 4: In each forward propagation of the training process, at each segmentation result f θ Calculate the Dice loss L between (x) and the segmentation label y dice And the weighted cross entropy loss L ce As the segmentation loss L seg , as shown in formula (1), f θ It should be noted that formula (1) is presented with the original medical image x as an example, and the enhanced medical image x ′ The same reasoning applies and will not be repeated here. θ (x) and enhanced segmentation result f θ (x ′) should remain consistent, so in order to improve the consistency of the segmentation results before and after enhancement, the relative entropy D is calculated between the two segmentation results as the consistency loss L consist , as shown in formula (2). Segmentation loss L seg and consistency loss L consist The weighted sum of the total weights can be used as the overall training loss L of the model overall , as shown in formula (3), where λ consist is the weighting coefficient.

[0094] L seg (x, y) = L dice (f θ (x), y) + L ce (f θ (x), y)(1);

[0095] L consist (x, y) = D(p(y|f θ (x))||p(y|f θ (x ′ )))(2);

[0096] L overall (x, y) = L seg (x, y) + λ consist *L consist (x, y)(3).

[0097] Step 5: Repeat steps 2-4 until the model converges. Then, the target medical image is input into the trained tumor segmentation model to obtain the target tumor segmentation result.

[0098] The above single-source domain generalization solution has at least the following advantages:

[0099] 1. While enhancing the style diversity of medical images, it also preserves intensity differences, allowing these features to be better recognized and utilized by the model, improving the model's tumor segmentation performance.

[0100] 2. The consistency loss based on relative entropy can better ensure the consistency of the segmentation results before and after enhancement, thereby improving the generalization of the model.

[0101] On this basis, to verify the effectiveness of the above-mentioned single-source domain generalization scheme, this scheme is used here to verify it on actual clinical images. T2 modality images from the public image set TCGA-CESC are used for training, T1 modality images from the TCGA-CESC image set are used for cross-modality segmentation testing, and T2 modality images from the private image set are used for cross-center segmentation testing. Comprehensive evaluation is performed using three indicators: Dice coefficient, ASD, and 95% HD. The evaluation results are shown in Table 1.

[0102] Table 1 Evaluation results

[0103]

[0104] It can be seen that compared with other models, the tumor segmentation model proposed in this example outperforms the best existing results in all three indicators and can achieve accurate tumor segmentation.

[0105] Figure 7 This is a structural block diagram of a model training device provided in an embodiment of the present invention, which is used to execute the model training method provided in any of the above embodiments. This device and the model training method of each of the above embodiments belong to the same inventive concept. For details not fully described in the embodiment of the model training device, please refer to the embodiment of the above model training method. Figure 7 The device may specifically include: an original medical image acquisition module 510, a region acquisition module 520, an enhanced medical image acquisition module 530 and a model training module 540.

[0106] The original medical image acquisition module 510 can be used to acquire the original medical image containing the tumor;

[0107] A region obtaining module 520 may be configured to determine a gradient map of the original medical image, obtain seed points based on local maxima in the gradient map, and segment the original medical image using the seed points to obtain at least two regions;

[0108] The enhanced medical image obtaining module 530 may be configured to generate an enhancement coefficient for each region to obtain a sub-region enhancement map, and perform image enhancement on the original medical image according to the sub-region enhancement map to obtain an enhanced medical image;

[0109] The model training module 540 may be used to train an initial segmentation model based on the original medical image and the enhanced medical image to obtain a tumor segmentation model.

[0110] Optionally, the region obtaining module 520 may include:

[0111] The region obtaining unit can be used to apply a preset watershed algorithm on the original medical image using a seed point to segment the original medical image into at least two regions.

[0112] Optionally, the above-mentioned model training device further includes:

[0113] a target enhancement strategy selection module for, after acquiring an original medical image containing a tumor, selecting a target enhancement strategy from the at least two image enhancement strategies based on respective selection probabilities preset for the at least two image enhancement strategies;

[0114] Accordingly, the region obtaining module 520 may be specifically configured to:

[0115] When the target enhancement strategy is a region enhancement strategy, a gradient map of the original medical image is determined, and a local maximum value in the gradient map is used as a seed point. The seed point is used to segment the original medical image to obtain at least two regions.

[0116] On this basis, optionally, the above-mentioned model training device may further include:

[0117] The enhanced medical image re-obtaining module is used to select a target enhancement strategy from at least two image enhancement strategies according to each selection probability, and when the target enhancement strategy is a global enhancement strategy, perform image enhancement on the original medical image as a whole to obtain an enhanced medical image.

[0118] Optionally, the model training module 540 may include:

[0119] A segmentation result obtaining submodule is used to input the original medical image into the initial segmentation model to obtain the original segmentation result, and to input the enhanced medical image into the initial segmentation model to obtain the enhanced segmentation result;

[0120] The model training submodule is used to calculate the consistency loss based on the original segmentation results and the enhanced segmentation results, and adjust the parameters in the initial segmentation model according to the consistency loss to obtain the tumor segmentation model.

[0121] On this basis, an optional model training submodule includes:

[0122] The consistency loss is obtained by a unit, which is used to calculate the relative entropy between the original segmentation result and the enhanced segmentation result, and the relative entropy is used as the consistency loss.

[0123] Alternatively, the above-mentioned model training device further includes:

[0124] A segmentation label acquisition module is used to obtain the segmentation label of the original medical image for the tumor;

[0125] A segmentation loss calculation module is used to calculate the original segmentation loss based on the segmentation label and the original segmentation result, and to calculate the enhanced segmentation loss based on the segmentation label and the enhanced segmentation result;

[0126] Correspondingly, the model training submodule includes:

[0127] The model training unit is used to obtain a training loss according to the original segmentation loss, the enhanced segmentation loss, and the consistency loss, and adjust the parameters in the initial segmentation model according to the training loss to obtain a tumor segmentation model.

[0128] Based on any of the above model training devices, optional:

[0129] For a target region in each region that includes at least a tumor, in the original medical image, an intensity difference between intensity information of the tumor in the target region and intensity information of objects other than the tumor in the target region is less than or equal to a corresponding intensity difference in the enhanced medical image;

[0130] The initial segmentation model performs tumor segmentation based on intensity differences.

[0131] The model training device provided in the embodiment of the present invention obtains an original medical image containing a tumor through an original medical image acquisition module; determines the gradient map of the original medical image through a region acquisition module, and then uses the local maximum value in the gradient map as a seed point to segment the original medical image using the seed point to obtain at least two regions, thereby realizing a region division process based on anatomical information; further, generates an enhancement coefficient for each region through an enhanced medical image acquisition module to obtain a sub-region enhancement map, and then performs image enhancement on the original medical image according to the sub-region enhancement map to obtain an enhanced medical image, thereby realizing image enhancement of a single region separately, that is, image enhancement according to anatomical information; thus, through the model training module, the initial segmentation model is trained based on the original medical image and the enhanced medical image to obtain a tumor segmentation model. The above-mentioned device performs a regional image enhancement process based on anatomical information during the model training process. This process divides the original medical image into different regions according to the different tissues and / or organs in the anatomical information, and then performs different image enhancements on each region at the regional level. While enhancing the image, the intensity difference between the tumor and objects other than the tumor in the tissue and / or organ where the tumor is located can be retained, so that the model can use the intensity difference feature to improve its segmentation performance, that is, train a tumor segmentation model that can be used for segmenting tumors, and achieve accurate tumor segmentation.

[0132] The model training device provided in the embodiment of the present invention can execute the model training method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0133] It is worth noting that in the embodiment of the above-mentioned model training device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0134] Figure 8 This is a structural block diagram of a tumor segmentation device provided in an embodiment of the present invention. The device is used to execute the tumor segmentation method provided in any of the above embodiments. The device and the tumor segmentation method of the above embodiments belong to the same inventive concept. For details not fully described in the embodiments of the tumor segmentation device, please refer to the embodiments of the above tumor segmentation method. Figure 8 The device may specifically include: a tumor segmentation model acquisition module 610 and a tumor segmentation module 620.

[0135] A tumor segmentation model acquisition module 610 is configured to acquire a target medical image containing a tumor and a tumor segmentation model trained according to the model training method described in any embodiment of the present invention;

[0136] The tumor segmentation module 620 is used to input the target medical image into the tumor segmentation model and obtain a target segmentation result of the target medical image for the tumor based on the output result of the tumor segmentation model.

[0137] The tumor segmentation device provided in the embodiment of the present invention achieves accurate segmentation of tumors in target medical images through the cooperation between the tumor segmentation model acquisition module and the tumor segmentation module.

[0138] The tumor segmentation apparatus provided in the embodiment of the present invention can execute the tumor segmentation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0139] It is worth noting that in the embodiment of the above-mentioned tumor segmentation device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0140] Figure 9A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention 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 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) 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 invention described and / or claimed herein.

[0141] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0142] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0143] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the model training method or the tumor segmentation method.

[0144] In some embodiments, the model training method or the tumor segmentation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the model training method or the tumor segmentation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the model training method or the tumor segmentation method in any other appropriate manner (e.g., by means of firmware).

[0145] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that 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.

[0146] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0147] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0148] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. 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 input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0149] 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 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: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0150] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0151] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0152] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A model training method, characterized in that: include: Acquire original medical images containing tumors; determining a gradient map of the original medical image, obtaining a seed point based on a local maximum in the gradient map, and segmenting the original medical image using the seed point to obtain at least two regions; generating an enhancement coefficient for each of the regions to obtain a subregion enhancement map, and performing image enhancement on the original medical image according to the subregion enhancement map to obtain an enhanced medical image; Inputting the original medical image into an initial segmentation model to obtain an original segmentation result, and inputting the enhanced medical image into the initial segmentation model to obtain an enhanced segmentation result; A consistency loss is calculated according to the original segmentation result and the enhanced segmentation result, and parameters in the initial segmentation model are adjusted according to the consistency loss to obtain a tumor segmentation model.

2. The method according to claim 1, characterized in that The step of segmenting the original medical image using the seed point to obtain at least two regions includes: Using the seed points, a preset watershed algorithm is applied to the original medical image to segment the original medical image into at least two regions.

3. The method according to claim 1, characterized in that After acquiring the original medical image containing the tumor, the method further includes: For the selection probabilities respectively preset for at least two image enhancement strategies, selecting a target enhancement strategy from the at least two image enhancement strategies according to each of the selection probabilities; Accordingly, determining the gradient map of the original medical image includes: In a case where the target enhancement strategy is a region-based enhancement strategy, a gradient map of the original medical image is determined.

4. The method according to claim 3, characterized in that After selecting the target enhancement strategy from at least two image enhancement strategies according to each of the selection probabilities, the method further includes: In the case where the target enhancement strategy is a global enhancement strategy, the original medical image is enhanced as a whole to obtain the enhanced medical image.

5. The method according to claim 1, characterized in that The calculating the consistency loss according to the original segmentation result and the enhanced segmentation result includes: The relative entropy between the original segmentation result and the enhanced segmentation result is calculated, and the relative entropy is used as the consistency loss.

6. The method according to claim 1, characterized in that Also includes: Obtaining a segmentation label of the original medical image for the tumor; Calculating an original segmentation loss according to the segmentation label and the original segmentation result, and calculating an enhanced segmentation loss according to the segmentation label and the enhanced segmentation result; Accordingly, adjusting the parameters of the initial segmentation model according to the consistency loss to obtain a tumor segmentation model includes: A training loss is obtained according to the original segmentation loss, the enhanced segmentation loss, and the consistency loss, and parameters in the initial segmentation model are adjusted according to the training loss to obtain a tumor segmentation model.

7. The method according to any one of claims 1 to 6, characterized in that: For a target region including at least the tumor in each of the regions, in the original medical image, an intensity difference between intensity information of the tumor in the target region and intensity information of objects other than the tumor in the target region is less than or equal to an intensity difference corresponding to the enhanced medical image; The initial segmentation model performs tumor segmentation based on the intensity difference.

8. A tumor segmentation method, characterized in that: include: Acquire a target medical image containing a tumor and a tumor segmentation model trained according to the model training method of any one of claims 1 to 7; The target medical image is input into the tumor segmentation model, and a target segmentation result of the target medical image for the tumor is obtained according to an output result of the tumor segmentation model.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor performs the model training method as described in any one of claims 1 to 7, or the tumor segmentation method as described in claim 8.

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