Brain tumor edema assessment method and device

Abstract

The embodiment of the invention relates to the technical field of medical treatment, and provides a brain tumor edema assessment method and device, and the method comprises the steps: obtaining image data of a user, determining an edema region in the image data, obtaining a signal intensity value corresponding to the edema region, and then determining a plurality of target regions corresponding to different signal intensity values; and then acquiring a pathological result of each target region of the edema region and establishing a corresponding relationship between the pathological results and the plurality of target regions. According to the method, the signal intensity value of the image data and the pathological result are combined. And pathological results can be conveniently applied to different brain tumor diagnosis and treatment scenes. Each target region has the corresponding pathological result, so that a doctor can conveniently determine excision or treatment plans of the multiple target regions according to the pathological results corresponding to the multiple target regions. And different areas of the edema area can be conveniently compared and analyzed, so that the heterogeneity of the edema area on the peripheral side of the brain tumor area can be conveniently analyzed.

Description

Technical Field

[0001] This application relates to the field of medical technology, and more specifically, to a method and apparatus for assessing brain tumor edema. Background Technology

[0002] Brain tumors are having an increasingly significant impact on people's lives. The following example uses gliomas, which are tumors originating from glial cells in the brain, and are the most common primary intracranial tumors. In my country, the annual incidence of gliomas is 50,000 to 80,000, and their 5-year mortality rate is second only to pancreatic and lung cancer among all cancers. Currently, clinical diagnosis mainly relies on imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Confirmation requires obtaining specimens through tumor resection or biopsy for histological and molecular pathological examination to determine the pathological grade and molecular subtype. Peritumoral edema (PTBE) refers to the increased water content in the nerve tissue surrounding a central nervous system tumor, and is relatively common in gliomas, meningiomas, and metastatic tumors. Clinical data shows that PTBE can affect tumor exposure during surgery, increase the difficulty of tumor resection, cause or worsen neurological dysfunction, and also cause or worsen increased intracranial pressure. Therefore, PTBE is closely related to patient symptoms and signs, the difficulty of surgery, and the occurrence of postoperative complications. Brain tumors with peritumoral edema (PTBE) have significantly increased mortality and disability rates. Gliomas often have indistinct borders and varying degrees of infiltration, leading to diverse morphologies and heterogeneous densities of peritumoral edema. Some clinical studies suggest that the morphology of peritumoral edema may be related to glioma survival, prognosis, or recurrence; however, current techniques do not provide information on the impact of different morphologies or densities of peritumoral edema on glioma survival. Summary of the Invention

[0003] This application provides a method and apparatus for assessing brain tumor edema, which can solve the technical problem of the inability to assess brain tumor edema.

[0004] In a first aspect, embodiments of this application provide a brain tumor edema assessment device, the device including a processor, the processor being configured to perform:

[0005] Acquire user image data;

[0006] Identify the edema region in the image data and obtain the signal intensity value corresponding to the edema region;

[0007] The edema region is divided into multiple target regions based on the signal intensity value, and each of the multiple target regions corresponds to a different range of signal intensity values;

[0008] The pathological results of each target region are obtained, and a correspondence between the pathological results and the multiple target regions is established. After acquiring the user's image data, the edema region in the image data is first identified, and the signal intensity value corresponding to the edema region is obtained. Then, based on the signal intensity value, the edema region is divided into multiple target regions, with different signal intensity value ranges corresponding to the multiple target regions. The obtained pathological results of the edema region are then associated with the multiple target regions corresponding to the edema region. This association facilitates the evaluation of the edema region around the brain tumor based on this association. Since the edema morphology varies and the density is uneven, the signal intensity values ​​corresponding to different target regions are different. By obtaining the pathological results of the target regions corresponding to different signal intensity values, the edema region can be evaluated.

[0009] In some embodiments, obtaining the pathological results of the edema area and establishing the correspondence between the pathological results and the plurality of target areas includes:

[0010] Multiple puncture target points are determined based on the multiple target areas, and each target area includes at least one puncture target point;

[0011] Based on the aforementioned multiple puncture target points, puncture samples were taken, and biopsies were performed on the samples to obtain pathological results.

[0012] In some embodiments, dividing the edema region into multiple target regions based on the signal intensity value includes:

[0013] The edema region is divided into multiple target regions based on the signal intensity value and signal intensity range corresponding to the edema region, and the multiple target regions correspond to different signal intensity value ranges.

[0014] In some embodiments, before dividing the edema region into multiple target regions based on the signal intensity value, the processor is further configured to perform:

[0015] A first distribution characteristic of the signal intensity values ​​within the edema region is determined, and multiple signal intensity value ranges are determined based on the first distribution characteristic.

[0016] In some embodiments, after dividing the edema region into multiple target regions based on the signal intensity value, the processor is further configured to perform:

[0017] A heat map corresponding to multiple target areas is drawn based on the signal intensity value range of multiple target areas, and the heat map is displayed by a control and display device.

[0018] In some embodiments, determining the edema region in the image data and obtaining the signal intensity value corresponding to the edema region includes:

[0019] The image data is segmented to obtain the segmentation mask corresponding to the edema region of the brain tumor in the image data.

[0020] Obtain the signal strength value of the image data corresponding to the segmentation mask.

[0021] In some embodiments, the processor is further configured to perform: acquiring the signal intensity value corresponding to the puncture target point, and determining a second distribution feature of the signal intensity value corresponding to the puncture target point in a plurality of target areas;

[0022] The pathological results of the target area are determined based on the second distribution characteristics and the pathological results corresponding to the puncture target point.

[0023] Secondly, a brain tumor edema assessment device is provided, comprising:

[0024] The acquisition module is used to acquire imaging data corresponding to the user's brain tumor.

[0025] The segmentation module is used to determine the edema region in the image data and obtain the signal intensity value corresponding to the edema region;

[0026] The segmentation module is used to divide the edema region into multiple target regions based on the signal intensity value, and the multiple target regions correspond to different signal intensity value ranges;

[0027] The association module is used to obtain the pathological results of each target area and establish the correspondence between the pathological results and the multiple target areas.

[0028] Thirdly, a method for assessing brain tumor edema is provided, applied to a processor of a brain tumor edema assessment device, the method comprising:

[0029] Acquire user image data;

[0030] Identify the edema region in the image data and obtain the signal intensity value corresponding to the edema region;

[0031] The edema region is divided into multiple target regions based on the signal intensity value, and each of the multiple target regions corresponds to a different range of signal intensity values;

[0032] Obtain the pathological results of each target area and establish the correspondence between the pathological results and the multiple target areas. Attached Figure Description

[0033] Figure 1 A flowchart illustrating a method for assessing brain tumor edema provided in an embodiment of this application;

[0034] Figure 2A schematic diagram illustrating the display of a pathological result provided in an embodiment of this application;

[0035] Figure 3 A schematic diagram of a brain tumor edema assessment device provided in an embodiment of this application;

[0036] Figure 4 This is a schematic diagram of the structure of a brain tumor edema assessment device provided in an embodiment of this application. Detailed Implementation

[0037] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0038] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0039] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0040] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0041] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0042] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0043] Brain tumor edema heterogeneity refers to the unevenness in the degree and distribution of edema in the edematous region surrounding a brain tumor. In this application, the brain tumor region refers to the distribution area of ​​tumor cells in the brain or brainstem, while the edema region refers to the edematous region formed by fluid accumulation in the brain tissue due to damage or stimulation. In brain tumors (such as gliomas), the active proliferation and metabolism of tumor cells lead to local tissue edema, forming edematous regions. Edema heterogeneity indicates that the degree and distribution of these edema regions within the tumor are uneven, and they may exhibit diversity due to differences in tumor cell density, vascular structure, exudation, and other factors. During or before surgery for brain tumors, the brain tumor region and the edematous region surrounding it are usually determined by segmentation. To obtain pathological results of the edematous region surrounding the brain tumor, samples are usually taken from the patient's brain tissue through surgery or other interventional methods for pathological examination. The above segmentation and biopsy processes are usually performed independently, resulting in a lack of effective correspondence between these segmentation results and pathological results, which is not conducive to analyzing the heterogeneity of the edematous region surrounding the brain tumor region.

[0044] To address the aforementioned issues, this application provides a method for assessing brain tumor edema. After acquiring the user's image data, the edema region in the image data is identified, and the signal intensity value corresponding to the edema region is obtained. Then, based on the signal intensity value, the edema region is divided into multiple target areas, with different signal intensity value ranges corresponding to the multiple target areas. The pathological results of the edema region are correlated with the multiple target areas of the edema region, facilitating the assessment of brain tumor edema based on this correlation. Here, different signal intensity values ​​can be used to perform heterogeneity analysis on different target areas of the edema region.

[0045] It should be understood that the sequence number of each step in this embodiment does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment. To illustrate the technical solution of this application, specific embodiments are described below.

[0046] Please refer to Figure 1 The diagram illustrates a flowchart of a brain tumor edema assessment method according to Embodiment 1 of this application. This method is applied to a brain tumor edema assessment device, which includes a processor capable of executing... Figure 1 The method shown includes the following steps:

[0047] S101. Obtain the user's image data.

[0048] Optionally, after a user undergoes imaging and pathological examinations at a hospital, and is diagnosed with a brain tumor (such as a glioma) based on these examinations, the imaging data corresponding to the imaging and pathological examinations is the imaging data corresponding to the brain tumor.

[0049] Optionally, the imaging data, also known as neurological examination images, includes computed tomography (CT) images and magnetic resonance (MR) images. MR images may include T1, T1CE, T2, and T2-FLAIR, where T1, T1CE, T2, and T2-FLAIR each represent different imaging techniques and sequences, and the imaging data is multimodal data.

[0050] S102. Identify the edema area in the image data and obtain the signal intensity value corresponding to the edema area.

[0051] Optionally, in S102, the determination of the edema region in the image data and the signal intensity value of the image data are not sequential. The signal intensity value of the image data can be obtained first, and then the signal intensity value corresponding to the edema region in the image data can be determined. Alternatively, the edema region in the image data can be obtained first, and then the signal intensity value of the edema region in the image data can be obtained.

[0052] Optionally, to obtain the edema region in the image data, it can be manually planned by the user, for example, by using the mouse to define the target area corresponding to the edema; this target area is the edema region. Alternatively, the image data can be segmented to obtain a segmentation mask corresponding to the edema region of the brain tumor in the image data. That is, the edema region surrounding the brain tumor in the image is segmented to obtain the segmentation mask corresponding to the edema region.

[0053] Optionally, the image data is multimodal data. Before segmenting the image data, multimodal data fusion can be performed on the image data to achieve the fusion of multimodal data from multiple modes.

[0054] Optionally, if the image data includes multiple images, the first image in the image data is segmented to obtain a segmentation mask corresponding to the brain tumor region; the second image in the image data is segmented to obtain a segmentation mask corresponding to the edema region surrounding the brain tumor.

[0055] For example, the t1ce image is segmented to obtain the segmentation mask corresponding to the brain tumor region, and the t2fliar image is segmented to obtain the segmentation mask corresponding to the edema region surrounding the brain tumor.

[0056] Optionally, the image data can be segmented using a pre-trained instance segmentation model. An instance segmentation model is a computer vision model that can segment each instance within each region, distinguishing between different instances. An instance segmentation model typically consists of three main parts: image input, instance segmentation processing, and segmentation output. After image input, the model usually uses backbone networks such as VGGNet or ResNet to extract image features. Then, the model performs object detection to determine the location and category of target instances, either segmenting within a selected region or performing semantic segmentation first and then distinguishing different instances.

[0057] Optionally, the instance segmentation model is 3DU-net.

[0058] Optionally, after segmenting the image data to obtain the segmentation mask corresponding to the edema region, the signal intensity value of the image data corresponding to the segmentation mask can be obtained.

[0059] Signal intensity (or image intensity) is a key parameter used to describe the brightness of individual pixels in image data. In MRI images, brighter areas typically correspond to higher signal intensity values, while darker areas correspond to lower signal intensity values.

[0060] S103. The edema area is divided into multiple target areas based on the signal intensity value, and the signal intensity value ranges corresponding to the multiple target areas are different.

[0061] For a specific tissue or lesion, the signal intensity value on an MRI image is closely related to its internal proton density, relaxation time, and other physical properties. Therefore, different edema areas can be distinguished by the signal intensity value.

[0062] Optionally, the different signal strength value ranges corresponding to multiple target areas here means that each target area has its own corresponding signal strength value range, but the signal strength value range corresponding to each target area is different from the signal strength value range corresponding to other target areas.

[0063] Optionally, the signal strength value range corresponding to each target area is different from the signal strength value range corresponding to other target areas, and the signal strength value ranges of any two target areas are different and there is no overlapping area; for example, the signal strength value range corresponding to target area A is [3,100], and the signal strength value range corresponding to target area B is [101,200].

[0064] S104. Obtain the pathological results of each target area and establish the correspondence between the pathological results and multiple target areas.

[0065] Optionally, multiple target areas in the edematous region of the brain tumor area of ​​the patient's brain tissue can be sampled separately through surgery or other interventional methods, and the samples can be subjected to pathological examination (e.g., sent to the pathology department for pathological examination) to obtain the pathological results of each target area in the edematous region and establish the correspondence between each pathological result and the corresponding target area.

[0066] Optionally, pathological results may include gene expression levels in different target areas, cell morphology and arrangement, characteristics of cell nuclei, degree of edema, and vascular condition.

[0067] Thus, after segmenting the edematous region of the brain tumor, multiple target areas corresponding to different signal intensity ranges are determined based on the segmentation results; then, the pathological results corresponding to each target area are obtained. This method combines image data segmentation with pathological results, facilitating the application of pathological results to different brain tumor diagnosis and treatment scenarios. Since each target area has a corresponding pathological result, it is convenient for doctors to determine resection or treatment plans for multiple target areas based on the pathological results corresponding to multiple target areas. It also facilitates comparative analysis of different regions of the edema area, enabling the analysis of the heterogeneity of the edema area surrounding the brain tumor region.

[0068] Furthermore, after obtaining the pathological results corresponding to each target area, the relationship between each target area and the corresponding pathological results can be quantitatively analyzed, and a treatment plan can be determined based on this relationship.

[0069] Optionally, in S104, the edema region is divided into multiple target areas based on the signal intensity value, including:

[0070] Based on the signal intensity value corresponding to the edema region and the preset signal intensity range, the edema region is divided into multiple target areas. Each target area corresponds to a different signal intensity value range, meaning that the signal intensity value range corresponding to each target area is different from the signal intensity value range corresponding to other target areas.

[0071] Optionally, the edema area can be divided into three target regions, including a target region corresponding to a higher range of signal intensity values, a target region corresponding to a lower range of signal intensity values, and a target region corresponding to a range of signal intensity values ​​between higher and lower. Here, higher and lower can correspond to preset signal intensity value ranges, respectively.

[0072] Optionally, the method further includes: determining a first distribution feature of signal intensity values ​​within the edema region, and determining multiple signal intensity value ranges based on the first distribution feature, wherein the first distribution feature may be the distribution characteristics of signal intensity values ​​within the edema region, such as the range of signal intensity values ​​within multiple different sub-regions of the edema region. For example, if the signal intensity values ​​of region A within the edema region are 8, 9, and 20, and the signal intensity values ​​of region B within the edema region are 30 and 40, then the signal intensity value ranges can be determined to be [0, 20] and [30, 40].

[0073] Optionally, after S104, the method further includes: drawing a heatmap corresponding to multiple target regions based on the signal intensity value ranges corresponding to multiple target regions, and controlling a display device to display the heatmap. The display device may be part of a brain tumor edema assessment device, or an external device connected to the brain tumor edema assessment device. Optionally, different signal intensity value ranges may be drawn with different colors to allow the user to distinguish between multiple target regions.

[0074] For example, the edema area includes multiple target regions: target region A, target region B, and target region C. These multiple target regions correspond to different signal intensity ranges, which can be represented by the intensity or type of color. The brain tumor edema assessment device displays this heat map, visually showcasing multiple target regions to facilitate subsequent puncture, biopsy, treatment, and target selection.

[0075] In some embodiments, during the segmentation processing of the image data in S102, in addition to segmenting the edema region surrounding the brain tumor, the brain tumor region itself is also segmented to obtain a segmentation mask corresponding to the brain tumor region. Then, the brain tumor region and the edema region can be distinguished based on the signal intensity value corresponding to the segmentation mask. Different color intensities are used to distinguish the brain tumor region and the edema region; different color intensities are also used to distinguish different target areas within the edema region.

[0076] It is easy to understand that after identifying multiple target areas, in order to obtain pathological results for these multiple target areas, samples can be taken from these target areas via puncture. Pathological analysis of the samples yields the pathological results. Therefore, S104 includes:

[0077] Multiple puncture points are determined based on multiple target areas, and each target area includes at least one puncture point.

[0078] Obtain the pathological results of each puncture target and establish the correspondence between the pathological results and multiple target areas.

[0079] Optionally, puncture sampling can be performed based on multiple puncture targets to obtain the pathological results of each puncture target. The pathological results can be obtained by performing a biopsy on the sample.

[0080] In this way, the edema area is divided into multiple target areas by different signal intensity values ​​corresponding to the edema area; multiple target areas are used to achieve targeted selection of puncture targets, that is, each target area includes at least one puncture target point, so that multiple target areas can be sampled; and the samples are subjected to pathological analysis to obtain the pathological results of each target area.

[0081] Optionally, there may be one or more puncture points within each target area. For example, the number of puncture points may be determined based on the size of the target area. If one of the target areas occupies a larger area, more puncture points may be selected; if one of the target areas occupies a larger area, fewer puncture points may be selected.

[0082] Optionally, the number and location of puncture targets can be determined based on the first distribution characteristics of the signal intensity values ​​of each target area. Here, the first distribution characteristics can be the distribution characteristics of the signal intensity values ​​of the target area. The range of signal intensity values ​​of the target area is divided into multiple sub-ranges of signal intensity values. If the multiple sub-ranges of signal intensity values ​​have obvious regionality, that is, different sub-ranges of signal intensity values ​​correspond to different areas, then puncture targets can be set from multiple different areas. If the area corresponding to one of the sub-ranges of signal intensity values ​​is large, then more puncture targets are selected. If the area corresponding to one of the sub-ranges of signal intensity values ​​is small, then fewer puncture targets are selected.

[0083] Optionally, puncture sampling can be performed based on multiple puncture target points, including: planning a puncture path based on multiple puncture target points to obtain a puncture prediction path; and performing puncture sampling on multiple puncture target points based on the puncture prediction path.

[0084] Thus, by planning the puncture path based on the puncture target points of multiple target areas, puncture can be performed through the predicted puncture path, allowing puncture sampling at multiple puncture target points. That is, by determining the puncture target points corresponding to the target areas and planning the path based on the puncture target points, the puncture path planning determines the predicted puncture path, enabling puncture of multiple target areas, thereby improving the accuracy and efficiency of puncture.

[0085] Optionally, since the edema area corresponds to multiple puncture target points, the puncture path planning can plan multiple puncture prediction paths so that multiple puncture target points can be punctured and sampled through multiple puncture prediction paths.

[0086] Optionally, after S105, the method further includes: controlling and displaying the signal intensity value range corresponding to each target area and the pathological results corresponding to each target area. By displaying the signal intensity value ranges and pathological results of multiple target areas with correlation, doctors can perform pathological analysis on the pathological results of multiple target areas corresponding to different signal intensity value ranges.

[0087] See Figure 2 , Figure 2 This is a schematic diagram illustrating the display of a pathological result as provided in an embodiment of this application. Figure 2 It includes four images. Figure 2 The edema area surrounding the central tumor region is divided into three different target areas. The display interface shows the location information (e.g., coordinates), signal intensity values, and corresponding pathological results of the three target areas. Figure 2 Midbrain tumor, thought to be a glioma. Figure 3 The pathological results showed the genotypes and gene expression levels of common molecular markers (IDH, 1p / 19q, MGMT promoter, EGFR, p53, etc.) in the edematous region surrounding the glioma. Figure 2 The displayed results allow doctors to combine the segmentation results of the edema area with the pathological results to assess the resectable range of the edema area in real time.

[0088] For example, if the expression level of the aquaporin AQP4 gene in target area A of the edema image in patient 1 is significantly higher than that in target areas B and C, it can be predicted that the patient's tumor growth tends to extend towards target area A in the edema image. When determining the extent of tumor resection, the physician can take this result into account, resecting the edema area corresponding to target area A, thus avoiding further damage during resection and preventing recurrence after tumor resection, greatly improving the patient's prognosis and survival. As another example, if patient 2 undergoes targeted biopsies of the edema areas corresponding to target areas A, B, and C, and the pathological examination results of the entire edema area are found to be essentially consistent and similar to those of a brain tumor area, then when designing the tumor resection plan, the entire edema and tumor area will be considered for resection to avoid tumor residue.

[0089] Optionally, after S105, the method further includes:

[0090] Obtain the signal intensity value corresponding to each puncture target point;

[0091] Then, the control display device simultaneously displays the signal intensity value range of multiple target areas, the signal intensity value corresponding to each puncture target point, and the pathological results corresponding to each puncture target point, so that users can intuitively see the pathological results corresponding to different puncture target points in the same target area.

[0092] Optionally, after S105, the method further includes: acquiring the signal intensity value corresponding to the puncture target point, and determining the second distribution characteristics of the signal intensity value corresponding to the puncture target point in multiple target areas; and determining the pathological results of the target area based on the second distribution characteristics and the pathological results corresponding to the puncture target point.

[0093] Since the puncture target point is only a part of the target area, the pathological results of other areas with the same or similar signal intensity values ​​as the puncture target point can be estimated based on the pathological results of the puncture target point.

[0094] Optionally, the second distribution feature here can be the proportion of pixels in the target area whose signal intensity values ​​are the same as those corresponding to the puncture target point. For example, if the pathological result corresponding to puncture target point A in the target area is similar to the pathological result of a brain tumor region, and the proportion of regions in the target area with the same signal intensity value as the puncture target point is 90%, then the pathological result of the target area can be determined to be similar to the pathological result of a brain tumor based on the pathological result of that puncture target point.

[0095] Optionally, the method further includes: after obtaining the pathological results corresponding to multiple target areas, obtaining the pathological results of the brain tumor region, determining the similarity between the pathological results corresponding to multiple target areas and the pathological results of the brain tumor region, and determining the recurrence trend of each target area based on the similarity.

[0096] Optionally, the method further includes: after obtaining the pathological results corresponding to multiple target areas, generating and displaying a recommended treatment plan based on the pathological results corresponding to the multiple target areas.

[0097] See Figure 3 The diagram shows a schematic of a brain tumor edema assessment device provided in an embodiment of this application; for ease of explanation, only the parts related to the embodiments of this application are shown.

[0098] The brain tumor edema assessment device may specifically include the following modules:

[0099] The acquisition module is used to acquire imaging data corresponding to the user's brain tumor.

[0100] The segmentation module is used to determine the edema region in the image data and obtain the signal intensity value corresponding to the edema region;

[0101] The segmentation module is used to divide the edema region into multiple target regions based on the signal intensity value, and the multiple target regions correspond to different signal intensity value ranges;

[0102] The association module is used to obtain the pathological results of each target area and establish the correspondence between the pathological results and the multiple target areas.

[0103] In some embodiments, the association module is further configured to:

[0104] Multiple puncture target points are determined based on the multiple target areas, and each target area includes at least one puncture target point;

[0105] Based on the aforementioned multiple puncture target points, puncture samples were taken, and biopsies were performed on the samples to obtain pathological results.

[0106] In some embodiments, the association module is further configured to:

[0107] Multiple puncture target points are determined based on the multiple target areas, and each target area includes at least one puncture target point;

[0108] Obtain the pathological results of each puncture target and establish the correspondence between the pathological results and the multiple target areas.

[0109] In some embodiments, the partitioning module is further configured to:

[0110] The edema region is divided into multiple target regions based on the signal intensity value and signal intensity range corresponding to the edema region, and the multiple target regions correspond to different signal intensity value ranges.

[0111] In some embodiments, the segmentation module is further configured to: determine a first distribution feature of signal intensity values ​​within the edema region, and determine a plurality of signal intensity value ranges based on the first distribution feature.

[0112] In some embodiments, the division module is further configured to: draw heat maps corresponding to multiple target areas based on the signal intensity value ranges corresponding to multiple target areas and control the display device to display the heat maps.

[0113] In some embodiments, the segmentation module is further configured to: obtain the signal intensity value corresponding to the puncture target point, and determine the second distribution characteristics of the signal intensity value corresponding to the puncture target point in multiple target areas; and determine the pathological results of the target area based on the second distribution characteristics and the pathological results corresponding to the puncture target point.

[0114] In some embodiments, the segmentation module is further configured to segment the image data to obtain a segmentation mask corresponding to the edema region of the brain tumor in the image data;

[0115] Obtain the signal strength value of the image data corresponding to the segmentation mask.

[0116] The brain tumor edema assessment device provided in this application embodiment can be applied in the foregoing method embodiment. For details, please refer to the description of the above method embodiment, which will not be repeated here.

[0117] Figure 4 This is a structural block diagram of a brain tumor edema assessment device provided in an embodiment of this application. Figure 4 As shown, the brain tumor edema assessment device 400 of this embodiment includes: a processor 410, a memory 420, and a computer program 430 stored in the memory 420 and executable by the processor 410, such as a brain tumor edema assessment prediction program. When the processor 410 executes the computer program 430, it implements the steps of each embodiment of the above-described brain tumor edema assessment methods, for example... Figure 1 As shown in 101 to 104, or, when the processor 410 executes the computer program 430, the above is implemented. Figure 3 The functions of each module in the corresponding embodiment.

[0118] For example, the computer program 430 may be divided into one or more modules, one or more of which are stored in the memory 420 and executed by the processor 410 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 430 in the brain tumor edema assessment device 400. For example, the computer program 430 may be divided into various unit modules, each with the specific functions described above.

[0119] The brain tumor edema assessment device 400 may include, but is not limited to, a processor 410 and a memory 420. Those skilled in the art will understand that... Figure 4 This is merely an example of a brain tumor edema assessment device 400 and does not constitute a limitation on the brain tumor edema assessment device 400. It may include more or fewer components than shown, or combine certain components, or different components. For example, the brain tumor edema assessment device may also include input / output devices, network access devices, buses, etc.

[0120] The processor 410 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0121] The memory 420 can be an internal storage unit of the brain tumor edema assessment device 400, such as a hard disk or RAM of the brain tumor edema assessment device 400. The memory 420 can also be an external storage device of the brain tumor edema assessment device 400, such as a plug-in hard disk, smart memory card, flash memory card, etc., equipped on the brain tumor edema assessment device 400. Furthermore, the memory 420 can include both internal and external storage units of the brain tumor edema assessment device 400.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0123] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the brain tumor edema assessment method as described in any of the above embodiments.

[0124] In some embodiments, this application provides a computer program product that, when run on a brain tumor edema assessment device, causes the brain tumor edema assessment device to perform the brain tumor edema assessment method described in any of the above embodiments. In the above embodiments, the descriptions of each embodiment have different focuses; parts not described in detail or in a particular embodiment can be referred to in the relevant descriptions of other embodiments.

[0125] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0130] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on an electronic device, the electronic device can implement the steps in the various method embodiments described above.

[0131] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A brain tumor edema assessment device, characterized in that, The brain tumor edema assessment device includes a processor, which is used to perform: Acquire user image data; Identify the edema region in the image data and obtain the signal intensity value corresponding to the edema region; The edema region is divided into multiple target regions based on the signal intensity value, and each of the multiple target regions corresponds to a different range of signal intensity values; Obtain the pathological results of each target area and establish the correspondence between the pathological results and the multiple target areas.

2. The apparatus as claimed in claim 1, characterized in that, The process of obtaining the pathological results of each target region and establishing the correspondence between the pathological results and the multiple target regions includes: Multiple puncture target points are determined based on the multiple target areas, and each target area includes at least one puncture target point; Obtain the pathological results of each puncture target and establish the correspondence between the pathological results and the multiple target areas.

3. The apparatus as described in claim 1, characterized in that, The process of dividing the edema region into multiple target areas based on the signal intensity value includes: The edema region is divided into multiple target regions based on the signal intensity value and signal intensity range corresponding to the edema region, and the multiple target regions correspond to different signal intensity value ranges.

4. The apparatus as described in claim 3, characterized in that, Before dividing the edema region into multiple target regions based on the signal intensity value, the processor is also configured to perform: A first distribution characteristic of the signal intensity values ​​within the edema region is determined, and multiple signal intensity value ranges are determined based on the first distribution characteristic.

5. The apparatus as claimed in claim 1, characterized in that, The step of determining the edema region in the image data and obtaining the signal intensity value corresponding to the edema region includes: The image data is segmented to obtain the segmentation mask corresponding to the edema region of the brain tumor in the image data. Obtain the signal strength value of the image data corresponding to the segmentation mask.

6. The apparatus as claimed in claim 1, characterized in that, After dividing the edema region into multiple target areas based on the signal intensity value, the processor is further configured to perform: Based on the signal intensity value range corresponding to multiple target areas, a heat map corresponding to multiple target areas is drawn, and the display device is controlled to display the heat map.

7. The apparatus as claimed in claim 1, characterized in that, The processor is also used to perform: Obtain the signal intensity value corresponding to the puncture target point, and determine the second distribution characteristics of the signal intensity value corresponding to the puncture target point in multiple target areas; The pathological results of the target area are determined based on the second distribution characteristics and the pathological results corresponding to the puncture target point.

8. The apparatus as claimed in claim 1, characterized in that, The processor is also used to perform: Obtain pathological results for the brain tumor region; Determine the similarity between the pathological results corresponding to the multiple target regions and the pathological results of the brain tumor region; The recurrence trend of each target region is determined based on the similarity, wherein the recurrence trend includes multiple different levels, and different similarity ranges correspond to different levels.

9. A brain tumor edema assessment device, characterized in that, include: The acquisition module is used to acquire the user's image data; The segmentation module is used to determine the edema region in the image data and obtain the signal intensity value corresponding to the edema region; The segmentation module is used to divide the edema region into multiple target regions based on the signal intensity value, and the multiple target regions correspond to different signal intensity value ranges; The association module is used to obtain the pathological results of each target area and establish the correspondence between the pathological results and the multiple target areas.

10. A method for assessing brain tumor edema, characterized in that, A processor for use in a brain tumor edema assessment device, the method comprising: Acquire user image data; Identify the edema region in the image data and obtain the signal intensity value corresponding to the edema region; The edema region is divided into multiple target regions based on the signal intensity value, and each of the multiple target regions corresponds to a different range of signal intensity values; Obtain the pathological results of each target area and establish the correspondence between the pathological results and the multiple target areas.

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