Method and device for determining boundary of damaged tissue, electronic equipment and storage medium

Abstract

The invention relates to a damaged tissue boundary determination method and device, electronic equipment and a storage medium. The damaged tissue boundary determination method comprises the following steps: acquiring a brain medical image; segmenting a focus area in the brain medical image, wherein the focus area comprises a cerebral ischemia area; performing multi-level surrounding tissue division on the periphery of the focus area by using a surrounding tissue division strategy; determining a damaged tissue boundary around the lesion area based on the key distinguishing features in the multi-level surrounding tissues; the brain tissue between the focus area and the damaged tissue boundary is an abnormal blood flow tissue area which is difficult to observe on the basis of a brain medical image, and the key distinguishing features can be used for distinguishing normal tissue and lesion tissue. The method has better robustness and detection precision.

Description

Technical Field

[0001] This disclosure relates to brain medical image processing technology, and in particular to a method and apparatus for determining the boundary of damaged tissue, electronic equipment and storage medium. Background Technology

[0002] In related technologies, the detection of lesions such as stroke typically relies on neurological imaging (such as CT and MRI). For example, when identifying ischemic lesions in stroke, the Tmax sequence is usually used, with threshold conditions such as a Tmax value greater than 6s or 4s used to define the ischemic lesion tissue. Different threshold selections in this method result in varying ischemic lesions, which can affect subsequent treatment and patient rehabilitation. Due to the complexity of brain tissue, an abnormality in one area will inevitably alter the blood flow in the surrounding tissues. The blood flow in this hidden tissue area can also affect the patient's recovery. Therefore, it is necessary to consider the surrounding tissue areas of this often-overlooked lesion tissue to fully utilize in subsequent patient treatment. Summary of the Invention

[0003] This disclosure presents a method, apparatus, electronic device, and storage medium for determining the boundary of damaged tissue, used for comprehensive analysis of brain tissue damage.

[0004] According to one aspect of this disclosure, a method for determining the boundary of damaged tissue is provided, characterized by comprising: acquiring brain medical images; segmenting lesion regions in the brain medical images, the lesion regions including cerebral ischemia regions; performing multi-level segmentation of the surrounding tissues around the lesion regions using a surrounding tissue segmentation strategy; determining the boundary of damaged tissues around the lesion regions based on key distinguishing features within the multi-level surrounding tissues; wherein the brain tissue between the lesion regions and the boundary of damaged tissues is a blood flow abnormality tissue region that is difficult to observe based on brain medical images, and the key distinguishing features can be used to distinguish normal tissues from diseased tissues.

[0005] In some possible implementations, the method further includes determining the key distinguishing features; determining the key distinguishing features includes: acquiring a first set of brain images; segmenting cerebral blood vessels and lesion regions from the first set of brain images; extracting vascular morphological features and dynamic blood flow features based on the lesion region, cerebral blood vessels, and a normal region on the symmetrical side of the lesion region, respectively; and determining key distinguishing features that can distinguish the normal region and the lesion region based on the vascular morphological features and the dynamic blood flow features.

[0006] In some possible implementations, vascular morphology features and dynamic blood flow features are extracted based on the lesion region, cerebral blood vessels, and normal regions on the symmetrical side of the lesion region, including: calculating morphological parameters such as vessel length, volume, cross-sectional area, and curvature of cerebral blood vessels in the lesion region and normal regions to obtain the vascular morphology features; and performing dynamic radiomics feature extraction and multi-dimensional feature screening processing on brain tissue in the lesion region and normal regions to obtain the dynamic blood flow features.

[0007] In some possible implementations, the method of using a surrounding tissue division strategy to divide the lesion area into multiple levels of surrounding tissue includes: expanding outward from the lesion area according to a preset voxel, with each expansion of a preset voxel resulting in a first-level surrounding area; obtaining the multi-level surrounding areas based on multiple expansions of preset voxels; and / or expanding outward according to preset voxels includes: using the voxel coordinates on the outer boundary of the lesion area as a reference, expanding outward to adjacent outer regions according to a preset number of voxels, with the expanded regions forming the first-level surrounding areas.

[0008] In some possible implementations, the step of using a surrounding tissue division strategy to divide the lesion area into multiple levels of surrounding tissue includes: based on the lesion area, expanding outward at a preset distance to form multiple preset shapes; the boundary of the lesion area and the first preset shape, as well as the adjacent preset shapes, constitute the multi-level surrounding area.

[0009] In some possible implementations, determining the boundary of damaged tissue around the lesion area based on key distinguishing features within the multi-level surrounding tissue includes: sequentially determining whether key distinguishing features within each level of the surrounding area satisfy preset conditions in order of distance from the lesion area; if the preset conditions are satisfied, identifying the corresponding surrounding area as damaged tissue, and continuing to determine whether the key distinguishing features of the next surrounding area satisfy the preset conditions, until the preset conditions are not satisfied, identifying the boundary of the corresponding surrounding area as the boundary of damaged tissue.

[0010] In some possible implementations, the step of sequentially determining whether the key distinguishing features in each of the surrounding areas meet preset conditions in order of distance from the lesion region includes at least one of the following methods: determining whether the similarity between the key distinguishing features in each of the surrounding areas and the key distinguishing features in the lesion region is greater than a first threshold; identifying the proportion of abnormal tissue in the surrounding areas based on the key distinguishing features in the surrounding areas and determining whether the proportion is greater than a second threshold; and determining whether the distance between the key distinguishing features in each of the surrounding areas and the key distinguishing features in the lesion region is less than a third threshold.

[0011] According to a second aspect of this disclosure, a damaged tissue boundary device is provided, comprising: an acquisition module for acquiring brain medical images; a segmentation module for segmenting lesion regions in the brain medical images, the lesion regions including cerebral ischemia regions; a division module for performing multi-level division of surrounding tissues around the lesion regions using a surrounding tissue division strategy; and a determination module for determining the damaged tissue boundary around the lesion regions based on key distinguishing features within the multi-level surrounding tissues; wherein the brain tissue between the lesion regions and the damaged tissue boundary is a blood flow abnormality tissue region that is difficult to observe based on brain medical images, and the key distinguishing features can be used to distinguish between normal tissue and diseased tissue.

[0012] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method described in any one of the first aspects.

[0013] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, characterized in that the computer program instructions, when executed by a processor, implement the method described in any one of the first aspects.

[0014] Based on the above configuration, this disclosure proposes a method for determining the boundary of damaged tissue. This method is applicable to processing brain medical images or other blood flow-related medical images. In addition to obtaining the ischemic brain region from brain medical images through medical processing and analysis, this disclosure further employs a surrounding tissue segmentation strategy to divide the brain lesion area into multiple levels of surrounding regions. Utilizing key distinguishing features within these surrounding regions that can identify blood flow abnormalities, the method further analyzes whether blood flow abnormalities or the risk of blood flow abnormalities exist within these regions, thereby determining whether these surrounding regions are hidden, difficult-to-detect areas of tissue damage with abnormal blood flow. Through the disclosed embodiments, the limitations of using threshold methods can be overcome, adapting to different brain images. Even when the data distribution and feature distribution of brain images change, effective detection results can still be achieved, demonstrating good generalization and robustness.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0016] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0018] Figure 1 This is a flowchart of the method for determining the boundary of damaged tissue in an embodiment of this disclosure. Detailed Implementation

[0019] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0020] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0021] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0022] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0023] The method disclosed herein can be executed by an image processing device, such as a terminal device, a server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some possible implementations, the method can be implemented by a processor calling computer-readable instructions stored in memory.

[0024] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0025] Figure 1 This is a flowchart of the method for determining the boundary of damaged tissue in this embodiment of the present disclosure; as follows: Figure 1 As shown, the method for determining the boundary of damaged tissue includes: S10: Acquire brain medical images, the brain medical images including perfusion sequences; In some possible implementations, brain medical images can be acquired in real time, for example, by real-time acquisition of images acquired by CT or MR imaging devices, or by receiving transmitted brain medical images via wired or wireless means, or by reading brain medical images from local or cloud server devices or other storage devices. This disclosure does not limit the scope of the implementation.

[0026] The brain medical images in this embodiment can be perfusion sequences, such as CTP, PWI, or other time-dependent four-dimensional brain images. However, this disclosure does not specifically limit them.

[0027] S20: Segment the lesion region in the brain medical image, the lesion region including the cerebral ischemia region; In some possible implementations, hemodynamic images, such as CBV, CBF, MTT, and Tmax, can be extracted from brain medical images using traditional hemodynamic analysis methods. Based on the condition that Tmax > 6s, the lesion region, i.e., the ischemic area of ​​the brain, can be obtained from the brain medical images.

[0028] S30: Using a surrounding tissue division strategy, perform multi-level surrounding tissue division around the lesion area; In some possible implementations, in order to confirm whether there is also tissue damage with abnormal blood flow in the tissue surrounding the lesion, this disclosure further divides the brain tissue surrounding the lesion into levels and analyzes the surrounding tissue at each level, thereby optimizing the outer boundary of the lesion area, that is, redefining the boundary of the damaged tissue based on the division of the lesion area.

[0029] S40: Based on the key distinguishing features within the multi-level surrounding tissues, determine the boundary of the damaged tissue surrounding the lesion area; the brain tissue between the lesion area and the boundary of the damaged tissue is a blood flow abnormality tissue area that is difficult to observe based on brain medical imaging, and the key distinguishing features can be used to distinguish between normal tissue and diseased tissue.

[0030] In some possible implementations, key distinguishing features that can differentiate whether blood flow is abnormal can be used to identify whether there is damage to the surrounding tissues at various levels around the lesion area. If damage is found, the tissue in that area is identified as damaged tissue. The process continues to determine whether there is damage to the surrounding tissues at the outermost level, and so on. In this way, the damage to the surrounding tissues of the lesion area can be identified sequentially until the first-level surrounding area is determined to be undamaged. At this point, the boundary of the damaged tissue can be determined based on that area.

[0031] Based on the above configuration, this embodiment of the present disclosure, after obtaining the cerebral ischemia region from brain medical images through medical processing and analysis, further utilizes a surrounding tissue segmentation strategy to divide the area around the brain lesion into multiple levels of surrounding regions. By employing key distinguishing features within these surrounding regions that can identify blood flow abnormalities, the present disclosure further analyzes whether blood flow abnormalities or the risk of blood flow abnormalities exist within these regions, thereby determining whether these surrounding regions are hidden, difficult-to-detect areas of tissue damage with abnormal blood flow. Through this disclosed embodiment, the limitations of using threshold methods can be overcome, adapting to different brain images. Even when the data distribution and feature distribution of brain images change, effective detection results can still be achieved, demonstrating good generalization and robustness.

[0032] The process of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings. The embodiments of this disclosure can first acquire brain medical images. These images can be dynamic perfusion images with time information, such as CTP, PWI, and other sequence images, or three-dimensional images, such as CT, ADC, DWI, T1, T2, and other images. This disclosure does not specifically limit these; any image that can be used to identify abnormal blood flow or tissue damage in brain tissue can be used as a brain image in the embodiments of this disclosure.

[0033] Specifically, the acquisition of brain medical images can include at least one of the following methods: ① directly acquiring images using medical imaging equipment (CT, MR); ② transmitting and receiving brain medical images via electronic devices; embodiments of this disclosure can receive images transmitted by other electronic devices via communication, and the communication method can include wired communication and / or wireless communication, which is not specifically limited here. ③ reading brain medical images stored in a database; embodiments of this disclosure can read locally stored image sequences or image sequences stored in a server according to received data reading instructions, which is not specifically limited here.

[0034] With access to brain medical images, the lesion regions of brain diseases can be segmented using these images. These brain diseases can include stroke, brain tumors, etc., but this disclosure does not specifically limit the scope of these diseases. Taking stroke as an example, with access to brain medical images, ischemic areas of the brain can be segmented from the images.

[0035] In some embodiments, the method for segmenting ischemic brain regions in this disclosure may include at least one of the following: ① Processing perfusion sequences such as CTP, PWI, etc., firstly obtaining the temporal density curve of each pixel in the selected layer, and then processing it through a mathematical model to obtain: cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), time to peak (TTP), and time to top (Tmax). Tmax represents the time it takes for the brain tissue's blood storage function to reach its maximum value, and is a sensitive indicator of changes in tissue perfusion and cerebral infarction. Brain tissue regions with Tmax greater than 6 seconds and less than 10 seconds can be defined as penumbras, i.e., ischemic brain regions. Correspondingly, the core infarction region is defined as brain tissue with Tmax greater than 10 seconds and a decrease in CBV. ② Processing brain medical images using a deep learning segmentation model to obtain ischemic brain regions. The segmentation model is a trained model that can be used for ischemic brain region segmentation. The specific training process will not be elaborated here. Those skilled in the art can use any of the following methods—strong supervision, semi-supervision, weak supervision, and unsupervision—to obtain the segmentation model used in the embodiments of this disclosure.

[0036] The processing method of the ischemic tissue segmentation model includes: performing frequency feature extraction processing on the input image to obtain a high-frequency feature group and a low-frequency feature group; performing a first fusion processing on the high-frequency feature group to obtain a first fusion feature, and performing a second fusion processing on the low-frequency feature group to obtain a second fusion feature; obtaining a third fusion feature based on the first fusion feature and the second fusion feature; and using the third fusion feature to detect the target object in the input image.

[0037] In some possible implementations, frequency feature extraction processing is performed on the input brain medical image to obtain a high-frequency feature group and a low-frequency feature group. The frequency feature extraction processing in this embodiment may include wavelet transform (DWT) processing; in other embodiments, Fourier transform and other methods may also be used, and this disclosure does not specifically limit this. After performing frequency feature extraction processing on the input image, a high-frequency feature group and a low-frequency feature group are obtained, wherein the high-frequency feature group includes at least two high-frequency feature maps, and the low-frequency feature group includes at least two low-frequency feature maps. That is, the high-frequency feature group and low-frequency feature group obtained in this embodiment each include at least two feature maps, representing the high-frequency and low-frequency features of the original input brain image. The high-frequency feature maps in the high-frequency feature group represent feature information at different high frequencies, and correspondingly, the low-frequency feature maps in the low-frequency feature group represent feature information at different low frequencies. Through the above configuration, feature information under different frequency conditions can be collected, and the richness of features can improve the accuracy of target detection.

[0038] In some possible implementations, performing a second fusion process on the low-frequency feature group to obtain a second fused feature includes: performing connection processing in the height and width directions on the low-frequency feature maps in the low-frequency feature group to obtain a first low-frequency connection feature and a second low-frequency connection feature; performing feature encoding processing on the first low-frequency feature and the second low-frequency feature to obtain a first encoded feature and a second encoded feature; and fusing the first encoded feature and the second encoded feature to obtain the second fused feature.

[0039] Specifically, the low-frequency feature maps within the low-frequency feature group are connected along the height direction to obtain the first low-frequency connected feature, and the low-frequency feature maps within the low-frequency feature group are connected along the width direction to obtain the second low-frequency connected feature. Given the first and second low-frequency connected features, a feature encoding module performs encoding processing to obtain encoding results under different feature connection modes, thus achieving multi-dimensional feature processing.

[0040] The feature encoding process includes: performing a two-branch process on the input connection features (a first low-frequency connection feature and a second low-frequency connection feature); wherein, the first branch process includes: performing a linear transformation and activation process on the connection features; the second branch process includes: performing a linear transformation, feature dimensionality reduction, activation process and state space transformation process on the connection features; and performing a product process and a linear transformation on the two features obtained from the two-branch process to obtain the encoded features corresponding to the connection features.

[0041] Taking the encoding process of the first low-frequency connection feature as an example, the processing of the second low-frequency feature is the same and will not be repeated. The encoding process of the first low-frequency connection feature includes: using the first branch to perform a linear transformation and activation processing (sigmoid) on the first low-frequency connection feature to obtain the first activation feature. Using the second branch to perform a linear transformation, feature dimensionality reduction (such as convolution), activation processing (sigmoid), and state space transformation on the first low-frequency connection feature to obtain the state space feature; performing a product processing and linear transformation on the first activation feature and the state space feature to obtain the encoded feature corresponding to the connection feature. The weight parameters used in the linear transformation of the first branch and the second branch are different; the specific parameters are learned during training. The convolution processing of the second branch uses a 1*1 kernel size, or it can also be 3*3; this disclosure does not specifically limit this. Furthermore, the model used in the state space transformation is the SSM model, and the state equation and output equation used are set by those skilled in the art according to their needs and are not specifically limited here. State space transformation can be used to achieve accurate representation of features over long distances, thereby improving model performance.

[0042] Given the first and second encoded features, they are fused to obtain the second fused feature. This can be achieved by performing dimensionality transformations on both the first and second encoded features to restore them to the original input image dimensions, then performing channel-wise concatenation to obtain the second fused feature based on the concatenation features. Alternatively, the concatenation features can be convolved and then summed with the low-frequency feature maps within the low-frequency feature group to obtain the second fused feature.

[0043] Based on the above configuration, low-frequency feature fusion processing can be achieved, resulting in accurate representation of low-frequency features over long distances, reducing feature loss and effectively improving feature expression.

[0044] In some possible implementations, performing a first fusion process on the high-frequency feature group to obtain a first fused feature includes: performing connection fusion processing on the high-frequency feature map of the high-frequency feature group in the height direction, width direction, and diagonal direction respectively to obtain high-frequency connection features; performing attention processing on the high-frequency connection features to obtain high-frequency attention features; and performing feature transformation processing on the high-frequency attention features to obtain the first fused feature.

[0045] The high-frequency feature maps of the high-frequency feature group are subjected to connection and fusion processing in the height, width, and diagonal directions to obtain high-frequency connection features. This includes: performing connection processing in the height, width, and diagonal directions on the high-frequency feature maps of the high-frequency feature group to obtain a first high-frequency connection feature, a second high-frequency connection feature, and a third high-frequency connection feature; and performing channel direction connection processing on the first, second, and third high-frequency connection features to obtain high-frequency connection features. This configuration can effectively integrate feature information within different high-frequency feature maps.

[0046] In some possible implementations, after obtaining the high-frequency connection features, the embodiments of this disclosure can further perform fusion optimization on the first fusion feature and the high-frequency connection features to obtain optimized high-frequency connection features. The fusion optimization method includes any of the following: ① performing channel-direction connection processing on the first fusion feature and the high-frequency connection features to obtain optimized high-frequency connection features; ② calculating the distance between the first fusion feature and the high-frequency fusion connection features; if the distance is less than a distance threshold, using the first fusion feature as the optimized high-frequency feature; if the distance is greater than the distance threshold, performing channel-direction connection processing on the first fusion feature and the high-frequency connection features to obtain optimized high-frequency connection features. With the optimized high-frequency connection features obtained, the optimized high-frequency connection features are used as high-frequency connection features for subsequent attention processing.

[0047] The high-frequency connection features are subjected to attention processing to obtain high-frequency attention features, including: performing recombination processing on the high-frequency connection features to obtain recombination features; performing three-branch processing on the recombination features to obtain first-branch features, second-branch features and third-branch features respectively; using the product of the second-branch features and the third-branch features to obtain first-branch product features; and using the product of the first-branch features and the first-branch product features to obtain high-frequency attention features.

[0048] The high-frequency connection features are recombined to obtain recombined features. This includes: performing block processing on the high-frequency connection features in the width and height directions to obtain multiple block features; and performing connection processing on the block features in the height direction to obtain the recombined features. The number of blocks can be set according to requirements, such as 2*2, or other numbers; this disclosure does not specifically limit this. By configuring the recombined features, feature information can be recombined, establishing associations and distinctions between feature information at different locations, thereby improving model performance.

[0049] The reconstructed features are processed in three branches to obtain first-branch features, second-branch features, and third-branch features. This includes: using the first and second high-frequency branches to sequentially perform feature centering, square root transformation, channel orientation standardization, and width and height plane standardization on the reconstructed features. Feature centering involves subtracting the mean from the reconstructed features to obtain features with a mean of 0 and a standard deviation of 1, following a standard normal distribution. Square root transformation applies the square root to the features, ensuring they conform to a normal distribution. Channel orientation standardization and width and height plane standardization normalize the features from different dimensions. The output of the third high-frequency branch is the reconstructed feature, which is directly used as the output of the third-branch feature for subsequent processing.

[0050] Furthermore, the product of the second branch features and the third branch features is used to obtain the first branch product features; the product of the first branch features and the first branch product features is used to obtain the high-frequency attention features. Given the high-frequency attention features, feature transformation processing is performed on them to obtain the first fused features. The feature transformation includes transforming the feature dimensions; through block processing in the width and height directions, the features are restored to the dimensions of the high-frequency features within the high-frequency feature group, thus obtaining the first fused features.

[0051] Based on the above configuration, high-frequency features are fused to obtain effective high-frequency features from different perspectives, thereby optimizing the algorithm accuracy.

[0052] Having obtained the first and second fusion features, a third fusion feature is further obtained by connecting the first and second fusion features along the channel direction. The location of the target object can then be obtained through the third fusion feature. Detecting the target object in the input image using the third fusion feature includes: directly performing activation processing on the third fusion feature to obtain the location information of the target object, wherein the location of the target object is where the feature value is greater than 0.5 in the activated feature. Alternatively, the location of the target object can be obtained by performing convolution processing on the third fusion feature followed by activation processing. This disclosure does not specifically limit this approach.

[0053] Based on the above configuration, the embodiments of this disclosure can accurately segment the cerebral ischemia lesion area, providing a good foundation for subsequent processing and helping to improve the accuracy of the algorithm.

[0054] Having obtained the lesion area, this embodiment of the disclosure further performs multi-level tissue division around the lesion area. That is, multiple levels of surrounding tissue areas are obtained sequentially around the lesion area in order from near to far.

[0055] In some possible implementations, the method of using a surrounding tissue segmentation strategy to perform multi-level surrounding tissue segmentation around the lesion region includes: expanding outward from the lesion region according to a preset number of voxels, with each expansion of a preset number of voxels forming a first-level surrounding region; and obtaining the multi-level surrounding regions based on multiple expansions of preset numbers of voxels. Specifically, expanding outward according to preset numbers of voxels includes: using the voxel coordinates on the outer boundary of the lesion region as a reference, expanding outward to adjacent outer regions according to a preset number of voxels, with the expanded regions forming the first-level surrounding regions.

[0056] The innermost region is the obtained lesion region. Starting from the outer edge of the lesion region, a preset voxel is extended to the adjacent peripheral region. For example, the number of preset voxels in this embodiment can be a region of [1, 10], but this is not a specific limitation of this disclosure. In this embodiment, the number of preset voxels can be 4. That is, by extending 4 voxels outward from the lesion region, a first-level peripheral region can be obtained. Then, starting from the outer periphery of the first-level peripheral region, preset voxels are extended to the adjacent outer side to obtain a second-level peripheral region; and so on, multiple levels of peripheral regions can be obtained successively. In other embodiments, at least two levels of peripheral regions can be obtained, and this disclosure does not specifically limit this. In addition, the layer expansion method in this embodiment can include an expansion method, and those skilled in the art can choose other methods to expand, and this disclosure does not limit this.

[0057] In other embodiments, the method of using a surrounding tissue division strategy to divide the lesion area into multiple levels of surrounding tissue includes: based on the lesion area, expanding outward at a preset distance to form multiple preset shapes; the boundary of the lesion area and the first preset shape, as well as the adjacent preset shapes, constitute the multi-level surrounding area.

[0058] In other words, in this embodiment, the area can be expanded outward according to the shape of a preset graphic to obtain multiple layers of surrounding areas. The preset shape can include circles, ellipses, squares, etc., expanding outward from the lesion area at a preset distance. Taking an ellipse as an example, the shape parameters of the ellipse can be set according to requirements. For example, the semi-minor axis of the ellipse can be set to a preset number of voxels, and the semi-major axis can be set to twice the preset number of voxels, where the preset number of voxels can be 4 voxels or other set values. In this way, multiple preset shapes can be successively expanded around the lesion area, and the distance between each preset shape can be a preset number of voxels or other set values. In other embodiments of this disclosure, other shapes can also be used for expansion, and this disclosure does not specifically limit this.

[0059] In addition, the embodiments of this disclosure may also set termination conditions for the division of the surrounding area, which may include at least one of the following: (1) When expanding the surrounding area outward, the expansion operation shall be terminated if the expanded area reaches or exceeds the image boundary; (2) The expansion operation is terminated when the number of expanded areas reaches the preset requirement. The preset requirement can be a number of expansions greater than 2, which can be set by those skilled in the art according to their needs.

[0060] Based on the above configuration, embodiments of this disclosure can obtain multiple surrounding tissue regions around the lesion region. With these surrounding tissue regions obtained, the boundary of damaged tissue around the lesion region can be determined based on key distinguishing features within the multi-level surrounding tissue regions. The brain tissue between the lesion region and the boundary of the damaged tissue is a region of abnormal blood flow that is difficult to observe based on brain medical imaging; the key distinguishing features can be used to differentiate between normal tissue and diseased tissue.

[0061] In some embodiments, the method for determining the key distinguishing features includes: acquiring a first set of brain images; segmenting cerebral blood vessels and lesion regions from the first set of brain images; extracting vascular morphological features and dynamic blood flow features based on the lesion region, cerebral blood vessels, and a normal region on the symmetrical side of the lesion region, respectively; and determining key distinguishing features that can distinguish the normal region from the lesion region based on the vascular morphological features and the dynamic blood flow features.

[0062] This disclosure embodiment utilizes brain symmetry mechanisms and feature statistics to obtain key distinguishing features. Specifically, a first brain image set can be acquired. The image type of the first brain image set can be the same as the brain medical image types described in the previous embodiments, such as PWI or CTP images. The acquisition method is also the same and will not be repeated here. The first brain image set includes multiple brain medical images; the more images, the more accurate the subsequent analysis results. This disclosure embodiment includes at least 20 examples, but this is only for illustrative purposes and no specific requirement is made.

[0063] Given a first set of brain images, cerebral blood vessels and lesion regions can be segmented from these images. This segmentation can be performed using the network structure described in the previous embodiments. In practice, the network model can be trained to segment cerebral blood vessels and lesion regions. Once the cerebral blood vessels and lesion regions are obtained, the principle of brain symmetry can be used to obtain the normal region on the symmetrical side of the lesion region, based on the brain symmetry line. Then, vascular morphological features and dynamic blood flow features can be extracted based on the lesion region, cerebral blood vessels, and the normal region on the symmetrical side of the lesion region. The method for determining the brain symmetry line includes first determining the image centroid and image deflection angle based on the brain images; the line passing through the centroid with the deflection angle as the direction is the brain symmetry line.

[0064] In addition, in this embodiment of the disclosure, vascular morphological features are extracted based on the lesion region, cerebral blood vessels, and the normal region on the symmetrical side of the lesion region, including: calculating morphological parameters such as vessel length, volume, cross-sectional area, and curvature of the cerebral blood vessels in the lesion region and the normal region to obtain the vascular morphological features; correspondingly, the vascular morphological features may include information such as vessel length, volume, cross-sectional area, curvature, and curvature. Other morphological features may also be included in other embodiments, and this disclosure does not specifically limit them.

[0065] Based on the lesion region, cerebral blood vessels, and the normal region symmetrical to the lesion region, dynamic blood flow features are extracted by performing dynamic radiomics feature extraction and multi-dimensional feature screening on the brain tissue of the lesion region and the normal region to obtain the dynamic blood flow features. The images in this embodiment are 4D images, composed of 3D images scanned at multiple time points. Therefore, this embodiment can extract radiomics features from the lesion region and the normal region of each 3D image separately, obtaining dynamic radiomics features at various time points within a continuous time period. In other words, the dynamic radiomics features are radiomics features of the lesion region and the normal region at multiple time points. Based on this, dynamic radiomics features of the lesion region and dynamic radiomics features of the normal region can be obtained.

[0066] Having obtained the dynamic radiomics features of both the lesion and normal regions, a multi-dimensional feature selection process is employed to identify dynamic blood flow features that can distinguish between the two regions. This multi-dimensional feature selection process includes: using a T-test to select significant features, for example, selecting features with a significance greater than 0.05; and using the Lasso algorithm to select dynamic blood flow features from these significant features. In other embodiments, clustering algorithms, analysis of variance, or other feature selection methods can also be used to obtain dynamic blood flow features; this disclosure does not specifically limit this approach. Based on the above process, the vascular morphology and dynamic blood flow features of the normal and lesion regions can be obtained. Combining these two types of features constitutes the key distinguishing features. The key distinguishing features obtained in this embodiment can effectively distinguish the vascular morphology and dynamic blood flow features of normal and abnormal regions, considering the possibility of tissue abnormalities from both static and dynamic perspectives, thus improving the accuracy of tissue differentiation.

[0067] Having obtained key distinguishing features that can differentiate between normal and abnormal tissue, the boundary of damaged tissue surrounding the lesion area can be determined based on the key distinguishing features within the multi-level surrounding tissues. This includes: sequentially judging whether the key distinguishing features within each level of the surrounding area meet preset conditions in order of distance from the lesion area; if the preset conditions are met, the corresponding surrounding area is identified as damaged tissue, and the judgment is continued on whether the key distinguishing features of the next surrounding area meet the preset conditions, until the boundary of the corresponding surrounding area is identified as the boundary of damaged tissue if the preset conditions are not met.

[0068] The step of judging whether the key distinguishing features in the surrounding areas at each level meet the preset conditions in order of distance from the lesion area, from near to far, includes at least one of the following methods: (1) Determine whether the similarity between the key distinguishing features in each of the surrounding areas and the key distinguishing features in the lesion area is greater than a first threshold; In some possible implementations, key distinguishing features of the surrounding tissue at each level can be sequentially acquired from near to far of the lesion region. The similarity between the key distinguishing features of the surrounding tissue at each level and the key distinguishing features of the lesion region can then be obtained. If the similarity is greater than a first threshold, the tissue in that surrounding region is determined to be damaged tissue, and the outer boundary of the corresponding surrounding region is the boundary of the damaged tissue. If the similarity is less than or equal to the first threshold, the key distinguishing features of the surrounding tissue at the next level are acquired and compared with the key distinguishing features of the lesion region until the damaged boundary is found. The first threshold can be a value greater than or equal to 0.5, which can be set by those skilled in the art according to their needs.

[0069] In some possible implementations, the similarity is determined by calculating at least one or more of the following: cosine similarity, adjusted cosine similarity, Pearson correlation coefficient, Jaccard similarity coefficient, log-likelihood similarity, and Tanimoto coefficient between key distinguishing features in the surrounding region and key distinguishing features in the lesion region. When multiple similarities are calculated, the mean of all similarities is used as the final similarity. The above is merely illustrative and does not constitute a specific limitation.

[0070] (2) Based on the key distinguishing features in the surrounding area, identify the proportion of abnormal tissue in the surrounding area and determine whether the proportion is greater than the second threshold.

[0071] In some possible implementations, an abnormal tissue proportion judgment model is first obtained. This model is then used to determine key distinguishing features within the surrounding area, identifying the proportion of abnormal tissue. If this proportion is greater than a second threshold, the surrounding area is determined to be damaged tissue, and its outer boundary is identified as the damaged tissue boundary. If the proportion is less than or equal to the second threshold, the key distinguishing features of the surrounding area at the next level are obtained, and the abnormal tissue proportion is judged again, until the damaged boundary is found. The second threshold can be a value greater than or equal to 0.6, which can be set by those skilled in the art according to their needs.

[0072] The abnormal tissue proportion judgment model can be a trained model capable of identifying abnormal proportions within tissues; this model can be a machine learning model or a deep learning model. This disclosure does not impose specific limitations on it. Generally, because different brain regions have different structures, if the proportion obtained by this abnormal tissue proportion model exceeds a third threshold, it can be determined that the brain tissue region is damaged.

[0073] (3) In some possible implementations, it is determined whether the distance between the key distinguishing features in each of the surrounding areas and the key distinguishing features in the lesion area is less than a third threshold; In some possible implementations, key distinguishing features of the surrounding tissue at each level can be sequentially acquired from near to far of the lesion region. The distance between the key distinguishing features of the surrounding tissue at each level and the key distinguishing features of the lesion region can then be obtained. If this distance is less than a third threshold, the tissue in that surrounding region is determined to be damaged tissue, and the outer boundary of the corresponding surrounding region is the boundary of the damaged tissue. If the similarity is greater than or equal to a first threshold, the key distinguishing features of the surrounding tissue at the next level are acquired and compared with the key distinguishing features within the lesion region until the damaged boundary is found. The third threshold can be a value greater than or equal to 0.5, which can be set by those skilled in the art according to their needs.

[0074] In some possible implementations, the distance is determined by calculating at least one or more of the following: Euclidean distance, normalized Euclidean distance, Mahalanobis distance, Manhattan distance, Chebyshev distance, Minkowski distance, and Hamming distance between key distinguishing features in the surrounding region and key distinguishing features in the lesion region. When multiple distances are calculated, the mean of all distances is used as the final distance. The above is merely illustrative and does not constitute a specific limitation herein.

[0075] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0076] In addition, this disclosure also provides a device for determining the boundary of damaged tissue, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any of the methods for determining the boundary of damaged tissue provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding section of the method and will not be repeated here.

[0077] This disclosure also provides a device for determining the boundary of damaged tissue, comprising: an acquisition module for acquiring brain medical images; a segmentation module for segmenting lesion regions in the brain medical images, the lesion regions including cerebral ischemia regions; a division module for performing multi-level division of surrounding tissues around the lesion regions using a surrounding tissue division strategy; and a determination module for determining the boundary of damaged tissues around the lesion regions based on key distinguishing features within the multi-level surrounding tissues; wherein the brain tissue between the lesion regions and the boundary of damaged tissues is a blood flow abnormality tissue region that is difficult to observe based on brain medical images, and the key distinguishing features can be used to distinguish between normal tissues and diseased tissues.

[0078] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0079] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0080] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A damaged tissue boundary determination method characterized by, include: Acquiring brain medical images; Segmenting the lesion region in the brain medical image, the lesion region including the cerebral ischemia region; The surrounding tissue is divided into multiple levels using a surrounding tissue division strategy; Based on the key distinguishing features within the multi-level surrounding tissues, the boundary of the damaged tissue surrounding the lesion area is determined; the brain tissue between the lesion area and the boundary of the damaged tissue is a blood flow abnormality tissue area that is difficult to observe based on brain medical imaging, and the key distinguishing features can be used to distinguish between normal tissue and diseased tissue.

2. The method of claim 1, wherein, The method further includes determining the key distinguishing features; determining the key distinguishing features includes: Obtain the first brain imaging set; Cerebral blood vessels and lesion areas were segmented from the first brain image set; Based on the lesion area, cerebral blood vessels, and normal areas on the symmetrical side of the lesion area, vascular morphology features and dynamic blood flow features are extracted respectively. Based on the vascular morphology features and the dynamic blood flow features, key distinguishing features that can differentiate between the normal area and the lesion area are determined.

3. The method of claim 2, wherein, Based on the lesion region, cerebral blood vessels, and normal regions symmetrically opposite the lesion region, vascular morphology features and dynamic blood flow features are extracted, including: The morphological parameters such as vessel length, volume, cross-sectional area, and curvature of the cerebral blood vessels in the lesion area and normal area are calculated to obtain the morphological characteristics of the blood vessels. Dynamic imaging omics feature extraction and multi-dimensional feature screening were performed on the brain tissue of the lesion area and normal area to obtain the dynamic blood flow features.

4. The method of claim 1, wherein, The method of using a surrounding tissue delineation strategy to perform multi-level surrounding tissue delineation around the lesion area includes: Based on the lesion area, expand outward according to a preset voxel, and obtain a first-level surrounding area for each preset voxel expansion; Based on the expansion of multiple preset voxels, the surrounding regions of the multi-level are obtained; and / or The expansion outward according to a preset voxel includes: Based on the voxel coordinates on the outer boundary of the lesion area, an extended region is formed to the adjacent outer side according to a preset number of voxels, and the extended region forms a primary surrounding area.

5. The method of claim 1, wherein, The method of using a surrounding tissue delineation strategy to perform multi-level surrounding tissue delineation around the lesion area includes: Based on the lesion area, multiple preset patterns are formed by expanding outward at preset distances; The boundary of the lesion area and the first preset pattern, as well as the adjacent preset patterns, constitute the surrounding area of ​​the multi-level system.

6. The method of claim 1, wherein, The determination of the damaged tissue boundary surrounding the lesion region based on key distinguishing features within the multi-level surrounding tissues includes: According to the order of distance from the lesion area to the farthest, determine whether the key distinguishing features in the surrounding areas at each level meet the preset conditions; If the preset conditions are met, the corresponding surrounding area is identified as damaged tissue, and the key distinguishing features of the next surrounding area are judged to meet the preset conditions until the preset conditions are not met, at which point the boundary of the corresponding surrounding area is identified as the boundary of damaged tissue.

7. The method of claim 6, wherein, The step of determining whether key distinguishing features in the surrounding areas at each level meet preset conditions in order of distance from the lesion region, from near to far, includes at least one of the following methods: Determine whether the similarity between key distinguishing features in each of the surrounding areas and key distinguishing features in the lesion area is greater than a first threshold; Based on key distinguishing features within the surrounding area, the proportion of abnormal tissue within the surrounding area is identified, and it is determined whether the proportion is greater than a second threshold. Determine whether the distance between the key distinguishing features in each of the surrounding areas and the key distinguishing features in the lesion area is less than a third threshold.

8. A damaged tissue boundary determination apparatus characterized by comprising: include: The acquisition module acquires brain medical images; A segmentation module segments the lesion region in the brain medical image, the lesion region including the cerebral ischemia region; The segmentation module utilizes a surrounding tissue segmentation strategy to perform multi-level segmentation of the surrounding tissues around the lesion area; The determination module determines the boundary of damaged tissue around the lesion area based on key distinguishing features within the multi-level surrounding tissues; the brain tissue between the lesion area and the boundary of the damaged tissue is a blood flow abnormality tissue area that is difficult to observe based on brain medical imaging, and the key distinguishing features can be used to distinguish between normal tissue and diseased tissue.

9. An electronic device, comprising: include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer program instructions, wherein, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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