Fiber tract automatic segmentation and quantitative labeling method for white matter abnormalities of parkinson's disease

Abstract

The application discloses a fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease, and belongs to the technical field of medical image processing. The application solves the problem that the existing technology relies on manual delineation or traditional machine learning methods for white matter abnormality detection, and has the problems of low efficiency and strong subjectivity. By setting a first threshold value and a second threshold value, not only can the abnormal fiber bundle be identified, but also the required threshold range can be selected according to different research purposes and clinical needs, so that reliable judgment results can be provided in both diagnosis and early screening. If both threshold values are selected, the method can comprehensively judge each parameter of each fiber bundle, so as to more accurately identify the white matter abnormal fiber bundle of the Parkinson's disease patient, improve the accuracy of the fiber bundle labeling result of the white matter abnormalities of Parkinson's disease, realize the function of high-precision positioning of the white matter abnormal fiber bundle of Parkinson's disease, and provide stronger support for clinical diagnosis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image processing, in particular to a fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease. BACKGROUND

[0002] Parkinson's disease is a neurodegenerative disease, and its pathological features include not only the degeneration of dopaminergic neurons in the substantia nigra, but also the microstructural abnormalities of white matter fiber bundles, such as the decrease in FA value shown by diffusion tensor imaging (DTI).

[0003] Currently, the detection of white matter abnormalities in the prior art relies on manual delineation or traditional machine learning methods, resulting in low efficiency and strong subjectivity. SUMMARY

[0004] The purpose of the present application is to provide a fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease, which can not only identify abnormal fiber bundles, but also select the required threshold range according to different research purposes and clinical needs, thereby ensuring reliable judgment results in both diagnosis and early screening; if both thresholds are selected, the method can make comprehensive judgments on each parameter of each fiber bundle, forming a more comprehensive conclusion, thereby more accurately detecting the abnormal white matter fiber bundles of Parkinson's disease patients, improving the accuracy of the fiber bundle labeling results of Parkinson's disease white matter abnormalities, providing stronger support for clinical diagnosis, and solving the problems raised in the above background art.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] The fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease comprises the following steps:

[0007] S1, collect diffusion magnetic resonance image data with normal fiber bundles, and unify all data to the same anatomical space to form a reference database;

[0008] S2, extract part of the data of the reference database as a healthy control group;

[0009] S3, perform automatic segmentation of the fiber bundle by the TractSeg algorithm, extract the main fiber bundle, calculate the fiber bundle parameters of the healthy control group using the fiber tracking algorithm, the fiber bundle parameters include fiber density, fiber cross section and fiber density-cross section product, and perform statistical analysis on the fiber bundle parameters of each healthy control group individual to calculate the mean and standard deviation;

[0010] S4, perform Z-score standardization processing on the fiber bundle parameters of the healthy control group for detecting abnormal fiber bundles;

[0011] S5, collecting diffusion magnetic resonance image data of the target Parkinson's disease patient, preprocessing the diffusion magnetic resonance image data thereof, and calculating fiber bundle parameters of the target Parkinson's disease patient using the step S3;

[0012] S6, comparing the fiber bundle parameters of the target Parkinson's disease patient with the Z-score standardized database of the healthy control group, calculating the Z-score value of each fiber bundle parameter, and labeling the fiber bundle whose Z-score value exceeds the preset threshold range as an abnormal fiber bundle;

[0013] S7, according to the position and characteristics of the abnormal fiber bundle, labeling it as a specific fiber bundle related to Parkinson's disease, mapping the Z-score value of the abnormal fiber bundle onto a heat map using a visualization tool, and generating detailed labeling information for each abnormal fiber bundle.

[0014] Further, in S6, the fiber bundle whose Z-score value exceeds the preset threshold range is labeled as an abnormal fiber bundle, comprising:

[0015] Extracting data samples from the Z-score standardized database of the healthy control group, and analyzing the Z-score distribution of different fiber bundle parameters;

[0016] According to the statistical analysis result, setting an initial threshold;

[0017] According to the initial threshold, combining research purposes and clinical needs, respectively setting a first threshold and a second threshold, the first threshold is used to reduce false positive detection, and the second threshold is used to improve sensitivity detection.

[0018] Further, in S6, the fiber bundle whose Z-score value exceeds the preset threshold range is labeled as an abnormal fiber bundle, further comprising:

[0019] Obtaining the fiber bundle parameters of the Parkinson's disease patient, and the research purposes and clinical needs, and determining to select the first threshold, the second threshold or both according to the research purposes and clinical needs;

[0020] When the first threshold is selected, if the Z-score value of any one parameter of any one fiber bundle exceeds the first threshold, the parameter of the fiber bundle is labeled as a high-confidence abnormality;

[0021] When the second threshold is selected, if the Z-score value of any one parameter of any one fiber bundle exceeds the second threshold but does not exceed the first threshold, the parameter of the fiber bundle is labeled as a low-confidence abnormality;

[0022] When both are selected, each parameter of each fiber bundle is compared with the first threshold value and the second threshold value respectively to obtain a comprehensive judgment result of each parameter of each fiber bundle.

[0023] Further, the diffusion magnetic resonance image data with normal fiber bundles in S1 is collected, and all data is unified to the same anatomical space to form a reference database, including:

[0024] The Z-score value of each fiber bundle parameter in the reference database is traversed and compared with the first threshold value and the second threshold value respectively to obtain a judgment result of each fiber bundle parameter;

[0025] Data samples with Z-score values of fiber bundle parameters in the reference database exceeding the first threshold value range are obtained as a first data set;

[0026] Data samples with Z-score values of fiber bundle parameters in the reference database exceeding the second threshold value range but not exceeding the first threshold value range are obtained as a second data set;

[0027] Two classification storage structures are created in the reference database, including: a first threshold value storage structure and a second threshold value storage structure;

[0028] The first data set and the second data set are matched to the corresponding data sample cache area respectively, and the information of each fiber bundle parameter stored is labeled, including: fiber bundle name, parameter type, Z-score value, corresponding threshold category and other related information.

[0029] Further, the fiber bundle parameters of the target Parkinson's disease patient in S5 are calculated using the steps in S3, including:

[0030] The preprocessed diffusion magnetic resonance image data of the target Parkinson's disease patient is obtained, and the corresponding healthy control group is matched, and the fiber tracking algorithm parameters of the healthy control group are obtained;

[0031] The fiber tracking algorithm parameters of the healthy control group are adjusted, and the diffusion tensor model is fitted for the diffusion magnetic resonance image data to obtain the diffusion tensor parameters of each voxel, including: anisotropy fraction, radial diffusion rate;

[0032] In the brain atlas of the standard space, a target region of the fiber bundle required for research is defined;

[0033] Starting from the seed point of interest in the target region, tracking is performed along the main diffusion direction until the termination condition is met;

[0034] During the tracking process, the voxel positions on the fiber path are recorded in real time.

[0035] Further, the step of calculating the fiber bundle parameters of the target Parkinson's disease patient in S5 using the step S3 further comprises:

[0036] Selecting a region of interest, counting the number of fiber paths passing through the region, and dividing the volume of the region to obtain the fiber density value;

[0037] Selecting a fiber path, counting the number of voxels intersecting the fiber path, and multiplying the volume of the voxels to obtain the fiber cross-sectional value;

[0038] Multiplying the fiber density value and the fiber cross-sectional value to obtain the fiber density-cross-sectional product;

[0039] Recording the personal information of the target Parkinson's disease patient and storing the calculated fiber bundle parameters in the personal information.

[0040] Further, the step of labeling the abnormal fiber bundle as a specific fiber bundle related to Parkinson's disease in S7 according to the location and characteristics of the abnormal fiber bundle comprises:

[0041] Obtaining the fiber path obtained by the fiber tracking algorithm, and explicitly labeling the name of the abnormal fiber bundle;

[0042] According to the name of the abnormal fiber bundle, determine the specific location of the abnormal fiber bundle in the brain;

[0043] According to the Z-score value of the fiber density, fiber cross-section and fiber density-cross-sectional product of the fiber bundle, determine the abnormality degree of the fiber bundle;

[0044] Create a three-dimensional brain template image, and map the Z-score value of each fiber bundle to the corresponding position;

[0045] Convert the Z-score value of the abnormal fiber bundle to a specific color and generate a heat map;

[0046] In the heat map, label the name, Z-score value and corresponding location information of each abnormal fiber bundle;

[0047] According to the abnormality of the fiber bundle parameters, generate a comprehensive conclusion.

[0048] Further, the step of collecting diffusion magnetic resonance image data with normal fiber bundles in S1 and unifying all data to the same anatomical space to form a reference database further comprises:

[0049] According to the research requirements and actual situation of data collection, set the update frequency of the reference database;

[0050] Using the Euclidean distance method, calculate the similarity between the new data sample and the historical data sample;

[0051] According to the similarity measurement result, a repetition rate of the new data sample and the historical data sample is calculated;

[0052] The data sample with a high repetition rate is identified, and the historical data sample with a high repetition rate from the new data sample is deleted from the reference database;

[0053] The new data sample is stored in the corresponding cache area to form an updated reference database.

[0054] Further, in S3, statistical analysis is performed on the fiber bundle parameters of each healthy control group individual to calculate the average value and standard deviation, including:

[0055] The same fiber bundle parameter value of each individual in all healthy control groups is obtained, the same fiber bundle parameter values of all individuals are added, and then divided by the number of individuals to obtain the average value of the parameter;

[0056] The square of the difference between each parameter value and the average value is calculated, summed and divided by the number of individuals, and then the square root is taken to obtain the standard deviation of the parameter.

[0057] Further, according to the abnormality of the fiber bundle parameters, a comprehensive conclusion is generated, including:

[0058] In the heat map, the abnormality degree and distribution of the abnormal fiber bundle are marked by different colors;

[0059] The generated heat map is inserted into the diagnosis report, and the marking information of each abnormal fiber bundle is listed for clinical use.

[0060] Further, the fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease further includes:

[0061] The fiber density, fiber cross section and fiber density-cross section product are extracted from the fiber bundle parameters of the target Parkinson's disease patient;

[0062] Based on the fiber density, fiber cross section and fiber density-cross section product, the fiber bundle abnormality degree index is calculated, and the calculation formula is as follows:

[0063]

[0064] Wherein, A is the fiber bundle abnormality degree index, ranging from 0 to 1, the higher the value, the higher the degree of fiber bundle abnormality; FD is the fiber density parameter, representing the number of fibers per unit volume in the fiber bundle of the target Parkinson's disease patient calculated by the S5 step, with units of per cubic millimeter, reflecting the density of the fiber bundle; FCS is the fiber cross-sectional parameter, representing the average cross-sectional area of the fiber bundle path calculated by the S5 step, with units of square millimeters, reflecting the spatial distribution characteristics of the fiber bundle; FDP is the fiber density-cross-sectional area product parameter, representing the product of FD and FCS, with units of dimensionless, reflecting the comprehensive structural characteristics of the fiber bundle; w1, w2, w3 are weight coefficients, corresponding to the weighted influence of fiber density, fiber cross-section and fiber density-cross-sectional area product respectively, ranging from 0 to 1, and satisfying w1+w2+w3=1, determined by statistical analysis of fiber bundle parameters of healthy control group and machine learning model training; Q is the data quality factor, representing the signal-to-noise ratio score of the diffusion magnetic resonance image data of the target Parkinson's disease patient, ranging from 0 to 10, the higher the value, the higher the data quality, calculated by weighting the image clarity and stereomicroscope calibration parameters evaluated in the preprocessing step; exp(-Q) is an exponential function, used to adjust the nonlinear influence of the data quality factor on the fiber bundle abnormality degree index, to ensure that the abnormality degree index is less sensitive when the data quality is poor;

[0065] According to the calculated fiber bundle abnormality degree index, the mapping relationship between the abnormality degree index and the abnormality level is set in advance to determine the abnormality level of each fiber bundle;

[0066] The determined abnormality level is combined with the corresponding fiber bundle name, location information and Z-score value to generate a dynamic assessment report of fiber bundle abnormality; In the dynamic assessment report, the abnormality degree index, abnormality level, fiber bundle name, Z-score value and corresponding anatomical location of each fiber bundle are listed;

[0067] Map the abnormality degree index in the dynamic assessment report to the three-dimensional brain template image;

[0068] The generated dynamic assessment report and three-dimensional brain template image are delivered to the visualization tool for clinical diagnosis and subsequent research;

[0069] The dynamic assessment report is stored in the patient database and associated with the patient's personal information and historical fiber bundle parameters, providing data support for long-term tracking of the progression of Parkinson's disease white matter abnormalities.

[0070] Further, the fiber bundle automatic segmentation and quantitative labeling method for Parkinson's disease white matter abnormalities further comprises:

[0071] extracting diffusion magnetic resonance image data collected at consecutive time points from a database of target Parkinson's disease patients, including fiber bundle parameter data at at least three time nodes;

[0072] preprocessing the fiber bundle parameter data at each time node, ensuring all data is unified to the same anatomical space, and using the TractSeg algorithm and fiber tracking algorithm in the S3 step to calculate fiber density, fiber cross-section, and fiber density-cross-section product for each time node respectively;

[0073] comparing the fiber bundle parameters at each time node with the Z-score standardized database of healthy controls, calculating the Z-score value of each fiber bundle parameter at each time node;

[0074] According to the Z-score value, identify the abnormal fiber bundle at each time node that exceeds the preset threshold range, and record its name, anatomical location and abnormality degree;

[0075] Time series analysis of Z-score values of the same fiber bundle at different time nodes, calculate the rate of change of Z-score value, the rate of change is calculated by the difference of adjacent time node Z-score value divided by the time interval, unit is Z-score change per month;

[0076] According to the rate of change of Z-score value, determine the dynamic trend of fiber bundle abnormalities;

[0077] Combine the dynamic trend of each fiber bundle with the corresponding Z-score value, fiber bundle name and anatomical location to generate a dynamic tracking report of fiber bundle abnormalities;

[0078] In the dynamic tracking report, list the Z-score value, rate of change, dynamic trend and corresponding anatomical location of each fiber bundle at each time node, and show the trend of Z-score value over time through a line chart;

[0079] According to the results of dynamic trend analysis, generate a prediction model of abnormal fiber bundles, the prediction model is based on time series analysis and machine learning algorithm, combining historical Z-score value and rate of change to predict the abnormality degree of fiber bundles in the future time node;

[0080] Combine the prediction results with the dynamic tracking report to generate a comprehensive trend analysis report, which includes the current abnormality degree, historical trend and future prediction results of each fiber bundle;

[0081] Store the comprehensive trend analysis report in the patient database, associate it with the patient's personal information and historical fiber bundle parameters, and provide data support for long-term dynamic monitoring and clinical intervention of Parkinson's disease white matter abnormalities.

[0082] Compared with the prior art, the present application has the following advantages:

[0083] In the present application, by setting the first threshold value and the second threshold value, not only can the abnormal fiber bundle be identified, but also the required threshold range can be selected according to different research purposes and clinical needs, thereby ensuring that reliable judgment results can be provided in both diagnosis and early screening; if both threshold values are selected for use at the same time, the method can make comprehensive judgments on each parameter of each fiber bundle, form a more comprehensive conclusion, and thus more accurately detect the white matter abnormal fiber bundle of the Parkinson's disease patient, improve the accuracy of the Parkinson's disease white matter abnormal fiber bundle labeling result, and realize the function of high-precision positioning of the Parkinson's disease white matter abnormal fiber bundle, thereby providing stronger support for clinical diagnosis. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 Flow chart of the Parkinson's disease white matter abnormal fiber bundle automatic segmentation and quantitative labeling method of the present application. DETAILED DESCRIPTION

[0085] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0086] To solve the problem in the prior art that the detection of white matter abnormalities relies on manual delineation or traditional machine learning methods, thereby resulting in low efficiency and strong subjectivity, please refer to Figure 1 The present embodiment provides the following technical solutions:

[0087] The Parkinson's disease white matter abnormal fiber bundle automatic segmentation and quantitative labeling method comprises the following steps:

[0088] S1, collect diffusion magnetic resonance image data with normal fiber bundles, and unify all the data to the same anatomical space to form a reference database; the conditions include: no history of nervous system diseases, no serious systemic diseases, age / gender matching with the target Parkinson's disease patient group, for example: if the average age of the target patient group is 65 years old, the gender ratio is 50% male and 50% female, then the healthy control group should also match this ratio as much as possible; the specific steps include:

[0089] Traverse the Z-score value of each fiber bundle parameter in the reference database, and compare it with the first threshold value and the second threshold value respectively to obtain the judgment result of each fiber bundle parameter;

[0090] obtaining data samples in which the Z-score value of the fiber bundle parameter in the reference database exceeds a first threshold range as a first data set;

[0091] obtaining data samples in which the Z-score value of the fiber bundle parameter in the reference database exceeds a second threshold range but does not exceed the first threshold range as a second data set;

[0092] creating two classification storage structures in the reference database, including: a first threshold storage structure and a second threshold storage structure;

[0093] matching the first data set and the second data set to the corresponding data sample cache area respectively, and labeling the information of each fiber bundle parameter stored, including: fiber bundle name, parameter type, Z-score value, corresponding threshold category and other related information such as age, gender, etc.;

[0094] setting the update frequency of the reference database according to the research needs and the actual situation of data collection; for example: updating every half year or every year; using the Euclidean distance method to calculate the similarity between the new data sample and the historical data sample; according to the similarity measurement result, calculating the repetition rate of the new data sample and the historical data sample; for example: if the similarity of a new sample and a historical sample exceeds a preset threshold (such as 95%), it is considered that the two samples are repeated; identifying data samples with high repetition rate, deleting historical data samples with high repetition rate from the reference database to avoid data redundancy; storing the new data sample in the corresponding cache area to form an updated reference database.

[0095] The beneficial effects achieved by the above content are: by classifying and storing the fiber bundle parameters according to the comparison results, the fiber bundle parameters in the reference database can be classified in detail, so that the required fiber bundle parameter information can be quickly indexed in actual application; not only helps to improve the accuracy of diagnosis, but also provides clear, systematic and comprehensive data support for subsequent analysis and research, greatly improving the usability and research efficiency of the fiber bundle parameters in the reference database.

[0096] S2, extracting part of the data of the reference database as a healthy control group; wherein the sampling data is not less than 30 samples, ensuring that the samples extracted are representative in age, gender and other key variables, and are matched with the target Parkinson's disease patient population.

[0097] S3, automatically segmenting the fiber bundle by the TractSeg algorithm to extract the main fiber bundle; calculating the fiber bundle parameters of the healthy control group using the fiber tracking algorithm, the fiber bundle parameters including: fiber density, fiber cross-section, and fiber density-cross-section product; and statistically analyzing the fiber bundle parameters of each healthy control group individual to calculate the mean and standard deviation, including: obtaining the same fiber bundle parameter value of each individual in all healthy control groups, adding the same fiber bundle parameter values of all individuals, and then dividing by the number of individuals to obtain the mean value of the parameter; calculating the square of the difference between each parameter value and the mean value, summing and dividing by the number of individuals, and then taking the square root to obtain the standard deviation of the parameter.

[0098] S4, Z-score standardization processing of the fiber bundle parameters of the healthy control group for detecting abnormal fiber bundles;

[0099] S5, collecting the diffusion magnetic resonance image data of the target Parkinson's disease patient, pre-processing the diffusion magnetic resonance image data of the target Parkinson's disease patient, the pre-processing including head motion correction, noise reduction, distortion correction, and bias field correction to improve image quality, and calculating the fiber bundle parameters of the target Parkinson's disease patient using the S3 step, including:

[0100] Obtaining the pre-processed diffusion magnetic resonance image data of the target Parkinson's disease patient and matching the corresponding healthy control group, and obtaining the fiber tracking algorithm parameters of the healthy control group, such as the starting threshold of fiber tracking, step length, etc.;

[0101] Adjusting the fiber tracking algorithm parameters of the healthy control group, fitting the tensor model to the diffusion magnetic resonance image data to obtain the diffusion tensor parameters of each voxel, including: anisotropy fraction, radial diffusion rate;

[0102] In the brain atlas of the standard space (such as: MNI space), the target area of the fiber bundle required for research is defined; for example, to extract the left corticospinal tract, the target area needs to be defined at the posterior limb of the internal capsule and the cerebral peduncle where it passes through;

[0103] Starting from the seed point of interest in the target area, tracking along the main diffusion direction until the termination condition is met, such as the FA value being lower than a certain threshold; in the tracking process, the voxel positions on the fiber path are recorded in real time; selecting the region of interest, such as the corpus callosum, internal capsule, etc., and counting the number of fiber paths passing through the region, and dividing by the volume of the region to obtain the fiber density value;

[0104] The fiber path is selected, the number of voxels intersecting the fiber path is counted, and the volume of the voxels is multiplied to obtain a fiber cross-sectional value; the fiber density value is multiplied by the fiber cross-sectional value to obtain a fiber density-cross-sectional product; the personal information of the target Parkinson's disease patient is recorded, and the calculated fiber bundle parameters are stored in the personal information, including: patient number, fiber bundle name, parameter value, etc., facilitating subsequent analysis.

[0105] S6, compare the fiber bundle parameters of the target Parkinson's disease patient with the Z-score standardized database of the healthy control group, calculate the Z-score value of each fiber bundle parameter, and label the fiber bundle whose Z-score value exceeds the preset threshold range as an abnormal fiber bundle, including:

[0106] Extract data samples from the Z-score standardized database of the healthy control group, and analyze the Z-score distribution of different fiber bundle parameters;

[0107] According to the statistical analysis result, set an initial threshold value; for example, according to the 95% confidence interval of the normal distribution, the initial threshold value is preliminarily set as |Z|>2.0;

[0108] According to the initial threshold value, in combination with the research purpose and clinical demand, a first threshold value and a second threshold value are respectively set, the first threshold value is used to reduce false positive detection, for example: |Z|>2.0, and the second threshold value is used to improve the sensitivity of detection, for example: |Z|>1.96; if the research purpose is to reduce false positive results and is a confirmed state, the first threshold value is selected; if the research purpose is to improve the sensitivity of diagnosis and is early screening, the second threshold value is selected; if the clinical demand requires comprehensive judgment of multiple fiber bundle parameters of the patient, both the first threshold value and the second threshold value are selected.

[0109] Obtain the fiber bundle parameters of the Parkinson's disease patient, and the research purpose and clinical demand, and determine to select the first threshold value, the second threshold value or both according to the research purpose and clinical demand;

[0110] When the first threshold value is selected, if the Z-score value of any one parameter of any one fiber bundle exceeds the first threshold value, the parameter of the fiber bundle is labeled as high confidence abnormality; for example, if the research purpose is to have been diagnosed with Parkinson's disease, and assuming that the Z-score value of the FA parameter of a certain fiber bundle of the patient is -2.5, which is less than the first threshold value of -2.0, it is labeled as high confidence abnormality;

[0111] When the second threshold value is selected, if the Z-score value of any parameter of any fiber bundle exceeds the second threshold value but does not exceed the first threshold value, the parameter of the fiber bundle is marked as a low-confidence abnormality; for example, if the research purpose is early screening of Parkinson's disease, and the Z-score value of the RD parameter of a certain fiber bundle of a patient is +1.98, in order to further determine the accuracy of the parameter comparison, the parameter of the patient is further compared with the first threshold value, and the result is greater than +1.96, which is the second threshold value, but less than +2.0, which is the first threshold value, and it is marked as a low-confidence abnormality;

[0112] When both are used, each parameter of each fiber bundle is compared with the first threshold value and the second threshold value respectively, and a comprehensive judgment result of each parameter of each fiber bundle is obtained; for example, if the Z-score value of the FA parameter (fractional anisotropy) of a certain fiber bundle is -2.5 (high-confidence abnormality), and the Z-score value of the RD parameter (radial diffusivity) is +2.8 (low-confidence abnormality), a comprehensive conclusion can be drawn that the FA of the fiber bundle is significantly reduced and the RD is significantly increased, indicating the presence of typical demyelination; for example, if the Z-score value of the FA parameter of a certain fiber bundle is -1.98 (low-confidence abnormality), and the Z-score value of the RD parameter is +2.05 (high-confidence abnormality), a comprehensive conclusion can be drawn that the FA of the fiber bundle may be reduced and the RD is significantly increased, indicating that there may be demyelination; thus more accurate pathological diagnosis information can be provided to help clinicians better understand the patient's condition.

[0113] The above-mentioned beneficial effects: by setting the first threshold value and the second threshold value, not only can abnormal fiber bundles be identified, but also the required threshold range can be selected according to different research purposes and clinical needs, thereby ensuring that reliable judgment results can be provided in both confirmed diagnosis and early screening; if both threshold values are selected, the method can make comprehensive judgments on each parameter of each fiber bundle, form a more comprehensive conclusion, and thus more accurately detect white matter abnormal fiber bundles in Parkinson's disease patients, improve the accuracy of the Parkinson's disease white matter abnormal fiber bundle labeling result, and realize the function of high-precision positioning of Parkinson's disease white matter abnormal fiber bundles, providing stronger support for clinical diagnosis.

[0114] S7、According to the location and characteristics of the abnormal fiber bundle, it is marked as a specific fiber bundle related to Parkinson's disease, such as corpus callosum, internal capsule, external capsule, etc., the Z-score value of the abnormal fiber bundle is mapped to a heat map using a visualization tool, and detailed labeling information is generated for each abnormal fiber bundle, including: fiber bundle name, abnormality degree (Z-score value), location, etc., the specific steps include:

[0115] Obtain the fiber paths obtained by the fiber tracking algorithm, and clearly label the names of the abnormal fiber bundles, such as "corpus callosum", "internal capsule", etc.; according to the name of the abnormal fiber bundle, determine the specific location of the abnormal fiber bundle in the brain; for example: the corpus callosum connects the main fiber bundle of the two hemispheres of the brain; the internal capsule is located between the basal ganglia and the thalamus, and contains important ascending and descending fibers; the external capsule is located outside the internal capsule and contains fibers connecting the cortex and basal ganglia; the corticospinal tract is a motor fiber bundle from the cerebral cortex to the spinal cord; the corticopontine tract is a fiber bundle from the cerebral cortex to the pons;

[0116] According to the fiber density, fiber cross-section and Z-score value of the product of fiber density-cross-section of the fiber bundle, determine the abnormality degree of the fiber bundle; for example: the FA parameter Z-score value of the left corticospinal tract is detected as -2.5 (high confidence abnormality), and the RD parameter Z-score value is +2.8 (high confidence abnormality);

[0117] Create a three-dimensional brain template image, and map the Z-score value of each fiber bundle to the corresponding location; convert the Z-score value of the abnormal fiber bundle into a specific color and generate a heat map; in the heat map, label the name, Z-score value and corresponding location information of each abnormal fiber bundle; the labeling information includes: fiber bundle name: left corticospinal tract; abnormality degree: FA significantly decreased (Z=-2.5), RD significantly increased (Z=+2.8); location: left cerebral hemisphere, from the central front to the medulla oblongata;

[0118] According to the abnormality of the fiber bundle parameters, generate a comprehensive conclusion; and in the heat map, mark the abnormality degree and distribution of the abnormal fiber bundle by different colors; insert the generated heat map into the diagnosis report, and list the labeling information of each abnormal fiber bundle for clinical use.

[0119] Working principle: Collect diffusion magnetic resonance image data of healthy controls to provide reliable reference for subsequent abnormality detection; secondly, use TractSeg algorithm for automatic segmentation and parameter calculation of fiber bundle to ensure the accuracy and efficiency of fiber bundle extraction; through Z-score standardization processing and double threshold comparison mechanism, the sensitivity and specificity of abnormality detection can be flexibly adjusted according to research purposes and clinical needs, effectively distinguishing high confidence and low confidence abnormal fiber bundles; in addition, the Z-score value of the abnormal fiber bundle is mapped to the heat map, and detailed labeling information is generated, which provides intuitive and comprehensive visualization results for clinicians and researchers, helps to quickly understand and apply detection results, can significantly improve the accuracy and efficiency of Parkinson's disease diagnosis, realizes the function of high-precision positioning of white matter abnormalities in Parkinson's disease, and provides strong support for early screening and diagnosis.

[0120] The method for automatic segmentation and quantitative labeling of white matter abnormalities in Parkinson's disease further comprises:

[0121] extracting fiber density, fiber cross-section and fiber density-cross-section product from the fiber bundle parameters of the target Parkinson's disease patient;

[0122] Based on the fiber density, fiber cross-section and fiber density-cross-section product, the fiber bundle abnormality degree index is calculated, and the calculation formula is as follows:

[0123]

[0124] Wherein, A is the fiber bundle abnormality degree index, the range is 0 to 1, the higher the value, the higher the fiber bundle abnormality degree; FD is the fiber density parameter, which represents the number of fibers per unit volume in the fiber bundle of the target Parkinson's disease patient calculated by S5 step, the unit is per cubic millimeter, reflecting the density of the fiber bundle; FCS is the fiber cross-section parameter, which represents the average cross-sectional area of the fiber bundle path calculated by S5 step, the unit is square millimeter, reflecting the spatial distribution characteristics of the fiber bundle; FDP is the fiber density-cross-section product parameter, which represents the product of FD and FCS, the unit is dimensionless, reflecting the comprehensive structure characteristics of the fiber bundle; w1, w2, w3 are weight coefficients, corresponding to the weighted influence of fiber density, fiber cross-section and fiber density-cross-section product respectively, the range is 0 to 1, and w1+w2+w3=1, which is determined by statistical analysis of fiber bundle parameters of healthy control group and machine learning model training; Q is the data quality factor, which represents the signal-to-noise ratio score of the diffusion magnetic resonance image data of the target Parkinson's disease patient, the range is 0 to 10, the higher the value, the higher the data quality, which is calculated by weighting the image clarity and stereomicroscope calibration parameters evaluated in the preprocessing step; exp(-Q) is an exponential function, which is used to adjust the nonlinear influence of data quality factor on fiber bundle abnormality degree index, to ensure the sensitivity of abnormality degree index is reduced when the data quality is poor;

[0125] According to the calculated fiber bundle abnormality degree index, the mapping relationship between the abnormality degree index and the abnormality grade is set in advance, and the abnormality grade of each fiber bundle is determined;

[0126] The determined abnormality grade is combined with the corresponding fiber bundle name, position information and Z-score value to generate a dynamic evaluation report of fiber bundle abnormality degree; In the dynamic evaluation report, the abnormality degree index, abnormality grade, fiber bundle name, Z-score value and corresponding anatomical position of each fiber bundle are listed;

[0127] Map the abnormality degree index in the dynamic evaluation report to the three-dimensional brain template image;

[0128] The generated dynamic assessment report and three-dimensional brain template image are delivered to a visualization tool for clinical diagnosis and subsequent research;

[0129] The dynamic assessment report is stored in the patient database, associated with the patient's personal information and historical fiber bundle parameters, providing data support for long-term tracking of the progression of white matter abnormalities in Parkinson's disease.

[0130] Working principle: By extracting fiber density (FD), fiber cross-section (FCS) and fiber density-cross-section product (FDP) from the fiber bundle parameters of the target Parkinson's disease patient, and combining the data quality factor (Q) to calculate the fiber bundle abnormality degree index (A), the quantitative evaluation of the fiber bundle abnormality degree is realized. Fiber density (FD) is defined as the number of fibers per unit volume (unit: per cubic millimeter), calculated by the fiber tracking algorithm in step S5, specifically tracking from the seed point of the region of interest along the main diffusion direction, counting the number of fiber paths passing through the region and dividing by the volume of the region. Fiber cross-section (FCS) is defined as the average cross-sectional area of the fiber bundle path (unit: square millimeter), obtained by counting the number of voxels intersecting the fiber path and multiplying by the voxel volume. Fiber density-cross-section product (FDP) is the product of FD and FCS (dimensionless), reflecting the comprehensive structural characteristics of the fiber bundle. The weight coefficients w1, w2, w3 (range 0 to 1, satisfying w1+w2+w3=1) are determined by statistical analysis of fiber bundle parameters of healthy control group and machine learning model (such as support vector machine or random forest) training, specific steps are: extract the statistical distribution of FD, FCS, FDP from the healthy control group database, calculate the contribution rate of each parameter to abnormal classification (such as through feature importance analysis), and optimize the weight allocation through cross-validation. Data quality factor (Q, range 0 to 10) is calculated by the signal-to-noise ratio score in the preprocessing step and the stereomicroscope calibration parameter, specifically: signal-to-noise ratio is obtained by image gray gradient variance analysis, stereomicroscope calibration parameter is calculated by comparing the pixel deviation of the image and the standard template, and the two are weighted and summed according to the preset ratio (such as 0.6:0.4). Abnormality degree index A is calculated by formula , where exp(-Q) is an exponential function used to non-linearly adjust the influence of Q on A, ensuring that the sensitivity of A is reduced when the data quality is low. According to the A value (range 0 to 1) and the preset mapping relationship (such as A<0.3 is normal, 0.3≤A≤0.6 is mild abnormality, 0.6≤A≤0.8 is moderate abnormality, and A≥0.8 is severe abnormality), the abnormality level is determined. Finally, the A value, abnormality level, fiber bundle name, Z-score value and anatomical location (such as corpus callosum, internal capsule) are integrated into the dynamic assessment report, and the A value is mapped through the three-dimensional brain template image, using the visualization tool (such as FSLView or MRtrix) to generate a heat map, and stored in the patient database associated with personal information (such as patient number, age).

[0131] Taking the left corticospinal tract of a 65-year-old male Parkinson's disease patient as an example, assuming that the FD calculated in the S5 step is 120 per cubic millimeter, the FCS is 2.5 square millimeters, and the FDP is 300 (FD x FCS). Through statistical analysis of the healthy control group (30 age- and gender-matched healthy individuals), the weight coefficients are determined as w1=0.4, w2=0.3, and w3=0.3, based on the random forest model training, in which the feature importance of FD, FCS, and FDP is 40%, 30%, and 30%, respectively. The pre-processing step evaluates the signal-to-noise ratio score as 8 (calculated by the gray level gradient variance), and the stereomicroscope calibration deviation is 0.2 millimeters. The two are weighted by 0.6:0.4 to obtain Q=8.2. Substituting the formula A=(0.4x120+0.3x2.5+0.3x300) / [1+exp(-8.2)], A≈0.65 is calculated, which is mapped to the moderate abnormality level. The dynamic assessment report is generated as follows: the fiber bundle name is "left corticospinal tract", the abnormality degree index A=0.65, the abnormality level is moderate, the Z-score value (obtained from S6 step) is FA=-2.5, RD=+2.8, and the anatomical location is "left cerebral hemisphere, from the precentral gyrus to the medulla oblongata". By using the FSLView tool, A=0.65 is mapped to orange and labeled on the left corticospinal tract area of the three-dimensional MNI space template to generate a heat map showing the abnormality degree and location. The report is stored in the patient database, associated with the patient number PD001 and personal information (such as age 65, male), facilitating subsequent clinical diagnosis and long-term tracking.

[0132] By calculating the fiber bundle abnormality degree index and generating a dynamic assessment report, this method realizes the quantitative evaluation and visual presentation of white matter abnormalities in Parkinson's disease. Combined with the weighted calculation of FD, FCS, FDP, and data quality factor Q, the abnormality degree index can comprehensively reflect the structural characteristics of the fiber bundle, and through nonlinear adjustment, it can reduce the risk of false judgment when the data quality is poor. The dynamic assessment report integrates multi-dimensional information (abnormality degree, Z-score value, anatomical location) and visually displays the abnormality distribution through a heat map, providing accurate diagnostic evidence for clinicians and significantly improving the accuracy and efficiency of white matter abnormality detection in Parkinson's disease, supporting early screening and long-term disease monitoring.

[0133] Further, the method for automatic segmentation and quantitative labeling of fiber bundles of white matter abnormalities in Parkinson's disease further comprises:

[0134] From the database of the target Parkinson's disease patient, extract the diffusion magnetic resonance image data collected at consecutive time points, including fiber bundle parameter data at at least three time nodes;

[0135] Preprocess the fiber bundle parameter data for each time node, ensure all data are unified to the same anatomical space, and use the TractSeg algorithm and fiber tracking algorithm in the S3 step to calculate the fiber density, fiber cross-section, and fiber density-cross-section product for each time node respectively;

[0136] Compare the fiber bundle parameters of each time node with the Z-score standardized database of the healthy control group, and calculate the Z-score value of each fiber bundle parameter at each time node;

[0137] According to the Z-score value, identify the abnormal fiber bundle that exceeds the preset threshold range at each time node, and record its name, anatomical location and abnormality degree;

[0138] Time series analysis is performed on the Z-score values of the same fiber bundle at different time nodes, and the rate of change of the Z-score value is calculated, which is calculated by the difference between the Z-score values of adjacent time nodes divided by the time interval, with the unit of Z-score change per month;

[0139] According to the rate of change of the Z-score value, determine the dynamic trend of the fiber bundle abnormality;

[0140] Combine the dynamic trend of each fiber bundle with the corresponding Z-score value, fiber bundle name and anatomical location to generate a dynamic tracking report of fiber bundle abnormalities;

[0141] In the dynamic tracking report, list the Z-score value, rate of change, dynamic trend and corresponding anatomical location of each fiber bundle at each time node, and show the trend of Z-score value over time through a line chart;

[0142] According to the results of dynamic trend analysis, generate a prediction model of abnormal fiber bundles, which is based on time series analysis and machine learning algorithm, combining historical Z-score values and rates of change to predict the abnormality degree of fiber bundles in the future time node;

[0143] Combine the prediction results with the dynamic tracking report to generate a comprehensive trend analysis report, which includes the current abnormality degree, historical trend and future prediction results of each fiber bundle;

[0144] Store the comprehensive trend analysis report in the patient database, associate it with the patient's personal information and historical fiber bundle parameters, and provide data support for long-term dynamic monitoring and clinical intervention of Parkinson's disease white matter abnormalities.

[0145] Working principle: Extract diffusion magnetic resonance image data of target Parkinson's disease patients at at least three time nodes, calculate fiber bundle parameters (FD, FCS, FDP) at each time node, and compare with Z-score standardized database of healthy control group to generate Z-score value, identify abnormal fiber bundle and its dynamic trend. Time node data is extracted from patient database, which needs to include diffusion magnetic resonance image at at least three time points (such as 6 month interval), preprocessing steps (head motion correction, noise reduction, distortion correction, bias field correction) are realized by FSL tool, and ensure that the data is unified to MNI standard anatomical space. Using S3 step TractSeg algorithm and fiber tracking algorithm, calculate FD (number of fibers per cubic millimeter, track along the main diffusion direction and divide by the volume of the region), FCS (square millimeter, number of statistical voxels multiplied by voxel volume) and FDP (FDxFCS) at each time node. Z-score value is calculated by comparing patient parameters with mean and standard deviation of healthy control group, formula is Z=(x-μ) / σ, where x is patient parameter, μ and σ are mean and standard deviation of healthy control group. Abnormal fiber bundle is identified by comparing Z-score value with first threshold (|Z|>2.0) and second threshold (|Z|>1.96), record name (such as corpus callosum), anatomical location (such as interthalamic adhesion) and abnormality degree (high / low confidence). Time series analysis calculates Z-score change rate, formula is ΔZ / Δt=(Zt2-Zt1) / (t2-t1), unit is Z-score change per month, reflecting abnormal progression speed. Dynamic trend is determined according to the change rate (such as ΔZ / Δt>0.1 for deterioration, -0.1≤ΔZ / Δt≤0.1 for stability, ΔZ / Δt<-0.1 for improvement). Based on historical Z-score value and change rate, use machine learning algorithm (such as ARIMA or LSTM) to train prediction model, predict Z-score value of next time node, specific steps are: take historical Z-score value and change rate as input, optimize model parameters (such as LSTM hidden layer unit number), evaluate prediction accuracy by cross-validation. Comprehensive trend analysis report integrates current abnormality degree (Z-score value), historical change rate, dynamic trend and prediction result, shows Z-score change with time through line chart, stores to patient database, associated with personal information (such as patient number, gender).

[0146] Take the same 65-year-old male Parkinson's disease patient as an example, extract his diffusion magnetic resonance image data at three time nodes of March 2024, September 2024, and March 2025. Preprocessing uses FSL tools for head motion correction (based on rigid registration), noise reduction (wavelet denoising), distortion correction (TOPUP algorithm), and bias field correction (N4 algorithm), and unifies to MNI space. For the left corticospinal tract, the TractSeg algorithm segments the fiber bundle, and the fiber tracking algorithm (step size 0.5 mm, FA termination threshold 0.1) calculates: T1 (March 2024) FD=120, FCS=2.5, FDP=300; T2 (September 2024) FD=115, FCS=2.4, FDP=276; T3 (March 2025) FD=110, FCS=2.3, FDP=253. Z-score values are calculated by comparing with the healthy control group (μFD=130, σFD=10, μFCS=2.8, σFCS=0.2, μFDP=364, σFDP=30), resulting in T1: ZFD=-1.0, ZFCS=-1.5, ZFDP=-2.13; T2: ZFD=-1.5, ZFCS=-2.0, ZFDP=-2.93; T3: ZFD=-2.0, ZFCS=-2.5, ZFDP=-3.7. ZFDP exceeds the first threshold (|Z|>2.0) at T2 and T3, and is marked as high confidence abnormality. The change rate is calculated as: T1-T2 (6 months) ΔZFDP=(-2.93-(-2.13)) / 6=-0.133, T2-T3 ΔZFDP=-0.123, average change rate-0.128, and is determined to be a deterioration trend. Using the LSTM model (hidden layer 100 units, trained for 100 rounds), the Z-score value and the change rate are used to predict T4 (September 2025) ZFDP≈-4.0. The comprehensive trend analysis report lists: fiber bundle name "left corticospinal tract", T1-T3 ZFDP values (-2.13, -2.93, -3.7), change rate (-0.128 / month), trend (deterioration), predicted ZFDP=-4.0, and location "left cerebral hemisphere, from the precentral to the medulla oblongata". The line chart is generated by Matplotlib, showing the downward trend of ZFDP over time. The report is stored in the patient database, associated with patient number PD001, supporting long-term monitoring and clinical intervention.

[0147] Through multi-time point fiber bundle parameter analysis and time series modeling, the method realizes the dynamic tracking and future trend prediction of white matter abnormalities in Parkinson's disease. The rate of Z-score change and dynamic trend analysis reveals the abnormal progression rate, and the prediction model provides a quantitative estimate of the future abnormality degree, providing a basis for the selection of clinical intervention timing. The comprehensive trend analysis report and the broken line chart intuitively present the dynamic changes of fiber bundle abnormalities, and the combination of patient personal information is stored in the database to support long-term disease monitoring and personalized treatment plan formulation, which significantly improves the accuracy and forward-looking of white matter abnormality diagnosis in Parkinson's disease.

[0148] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any actual such relationship or order between or among the entities or actions. Also, the terms "comprises", "comprising", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0149] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, alternatives, and variations can be made in the embodiments without departing from the spirit and scope of the present application as defined by the appended claims and their equivalents.

Claims

1. A method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease, characterized in that, Includes the following steps: S1. Collect diffusion magnetic resonance imaging data with normal fiber bundles and unify all data into the same anatomical space to form a reference database, which includes: The Z-score values ​​of each fiber bundle parameter in the reference database are traversed and compared with the first threshold and the second threshold respectively to obtain the judgment result of each fiber bundle parameter; data samples of fiber bundle parameters in the reference database whose Z-score values ​​exceed the first threshold range are obtained as the first dataset; Data samples from the reference database whose Z-score values ​​of fiber bundle parameters exceed the second threshold range but do not exceed the first threshold range are obtained and used as the second dataset. Two classification storage structures are created in the reference database, including a first threshold storage structure and a second threshold storage structure. The first dataset and the second dataset are matched to their respective data sample cache areas, and the information of each stored fiber bundle parameter is labeled, including: fiber bundle name, parameter type, Z-score value, and corresponding threshold category. Based on research needs and the actual situation of data collection, the update frequency of the reference database is set; the Euclidean distance method is used to calculate the similarity between new data samples and historical data samples; based on the similarity measurement results, the repetition rate between new data samples and historical data samples is calculated; data samples with high repetition rates are identified, and historical data samples with high repetition rates with new data samples are deleted from the reference database; new data samples are stored in the corresponding cache area to form the updated reference database. S2. Extract a portion of the data from the reference database to serve as a healthy control group; S3. Automatic segmentation of fiber bundles is performed using the TractSeg algorithm to extract the main fiber bundles; fiber bundle parameters of the healthy control group are calculated using the fiber tracking algorithm, including fiber density, fiber cross-section, and fiber density-cross-section product; and statistical analysis is performed on the fiber bundle parameters of each individual in the healthy control group to calculate their mean and standard deviation. S4. The fiber bundle parameters of the healthy control group were standardized using Z-score to detect abnormal fiber bundles. S5. Collect diffusion magnetic resonance imaging data of the target Parkinson's disease patient, preprocess the diffusion magnetic resonance imaging data, and use step S3 to calculate the fiber bundle parameters of the target Parkinson's disease patient. S6. Compare the fiber bundle parameters of the target Parkinson's disease patients with the Z-score standardized database of the healthy control group, calculate the Z-score value of each fiber bundle parameter, and mark the fiber bundles whose Z-score values ​​exceed the preset threshold range as abnormal fiber bundles. S7. Based on the location and characteristics of the abnormal fiber bundles, label them as specific fiber bundles associated with Parkinson's disease. Use visualization tools to map the Z-score values ​​of the abnormal fiber bundles onto a heatmap, and generate detailed labeling information for each abnormal fiber bundle, including: Obtain the fiber path obtained through the fiber tracing algorithm and clearly label the name of the abnormal fiber bundle; determine the specific location of the abnormal fiber bundle in the brain based on the name of the abnormal fiber bundle; determine the degree of abnormality of the fiber bundle based on the fiber density, fiber cross-section, and Z-score value of the product of fiber density and cross-section. Create a 3D brain template image and map the Z-score value of each fiber bundle to its corresponding location; convert the Z-score value of the abnormal fiber bundles to a specific color and generate a heatmap; label the name, Z-score value, and corresponding location information of each abnormal fiber bundle in the heatmap. Based on the abnormalities in fiber bundle parameters, a comprehensive conclusion is generated, including: marking the degree and distribution of abnormal fiber bundles with different colors in the heat map; inserting the generated heat map into the diagnostic report and listing the annotation information for each abnormal fiber bundle for clinical use.

2. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 1, characterized in that, In S6, fiber bundles with Z-score values ​​exceeding a preset threshold range are marked as abnormal fiber bundles, including: Data samples were extracted from the Z-score normalized database of the healthy control group to analyze the Z-score distribution of different fiber bundle parameters; Based on the statistical analysis results, set an initial threshold; Based on the initial threshold, and in conjunction with the research objectives and clinical needs, a first threshold and a second threshold are set respectively. The first threshold is used to reduce false positive detections, and the second threshold is used to improve the sensitivity of detection.

3. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 2, characterized in that, S6 further includes classifying fiber bundles with Z-score values ​​exceeding a preset threshold as abnormal fiber bundles, and also includes: Obtain fiber bundle parameters from Parkinson's disease patients, as well as research objectives and clinical needs, and determine the selection of the first threshold, the second threshold, or both based on research objectives and clinical needs; When selecting the first threshold, if the Z-score value of any parameter of any fiber bundle exceeds the first threshold, then that parameter of the fiber bundle is marked as a high-confidence anomaly. When selecting the second threshold, if the Z-score value of any parameter of any fiber bundle exceeds the second threshold but does not exceed the first threshold, then that parameter of the fiber bundle is marked as a low-confidence anomaly. When both are used, each parameter of each fiber bundle is compared with the first threshold and the second threshold respectively to obtain a comprehensive judgment result for each parameter of each fiber bundle.

4. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 1, characterized in that, S5 uses step S3 to calculate fiber bundle parameters for the target Parkinson's disease patient, including: Acquire preprocessed diffusion magnetic resonance imaging data of target Parkinson's disease patients, match them with corresponding healthy control groups, and obtain fiber tracking algorithm parameters for the healthy control groups; The fiber tracking algorithm parameters of the healthy control group were adjusted, and a tensor model was fitted to its diffusion magnetic resonance image data to obtain the diffusion tensor parameters of each voxel, including: anisotropy fraction and radial diffusion rate. In a standard spatial brain atlas, the target region of the fiber tract to be studied is defined; Starting from the seed point of interest in the target region, trace along the main diffusion direction until the termination condition is met; During the tracking process, the voxel positions along the fiber path are recorded in real time.

5. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 4, characterized in that, S5 uses step S3 to calculate fiber bundle parameters for target Parkinson's disease patients, and also includes: Select the region of interest, count the number of fiber paths passing through the region, and divide by the volume of the region to obtain the fiber density value; Select a fiber path, count the number of voxels that intersect with the fiber path, and multiply the number by the volume of the voxels to obtain the fiber cross-section value. Multiply the fiber density value by the fiber cross-sectional area value to obtain the fiber density-cross-sectional area product; Record the personal information of the target Parkinson's disease patient and store the calculated fiber bundle parameters in the personal information.

6. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 1, characterized in that, In S3, the fiber bundle parameters of each healthy control individual were statistically analyzed, and their mean and standard deviation were calculated, including: Obtain the same fiber bundle parameter value for each individual in all healthy control groups, add up the same fiber bundle parameter values ​​for all individuals, and then divide by the number of individuals to obtain the average value of the parameter. Calculate the square of the difference between each parameter value and the mean, sum them, divide by the number of individuals, and then take the square root to obtain the standard deviation of the parameter.

7. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 1, characterized in that, Also includes: Fiber density, fiber cross-section, and fiber density-cross-section product were extracted from fiber bundle parameters of target Parkinson's disease patients. The fiber bundle anomaly index is calculated based on fiber density, fiber cross-section, and the product of fiber density and cross-section. The calculation formula is as follows: Where A is the fiber bundle abnormality index, ranging from 0 to 1, with higher values ​​indicating greater fiber bundle abnormality; FD is the fiber density parameter, representing the number of fibers per unit volume within the fiber bundle of the target Parkinson's disease patient calculated through step S5, in millimeters per cubic meter, reflecting the density of the fiber bundle; FCS is the fiber cross-sectional area parameter, representing the average cross-sectional area of ​​the fiber bundle path calculated through step S5, in millimeters per square meter, reflecting the spatial distribution characteristics of the fiber bundle; FDP is the fiber density-cross-sectional area product parameter, representing the product of FD and FCS, in dimensionless units, reflecting the comprehensive structural characteristics of the fiber bundle; w1, w2, and w3 are weighting coefficients, respectively corresponding to... The weighted effects of fiber density, fiber cross-section, and the fiber density-cross-section product, ranging from 0 to 1 and satisfying w1+w2+w3=1, were determined by statistical analysis of fiber bundle parameters from healthy control groups and training of machine learning models. Q is the data quality factor, representing the signal-to-noise ratio score of the diffusion magnetic resonance imaging data of the target Parkinson's disease patients, ranging from 0 to 10. A higher value indicates higher data quality, and it is calculated by weighting the image sharpness and stereomicroscope calibration parameters assessed in the preprocessing step. exp(-Q) is an exponential function used to adjust the nonlinear effect of the data quality factor on the fiber bundle abnormality index, ensuring that the sensitivity of the abnormality index is reduced when the data quality is poor. Based on the calculated fiber bundle anomaly index, and by comparing it with the pre-set mapping relationship between the anomaly index and the anomaly level, the anomaly level of each fiber bundle is determined. The determined abnormality level is combined with the corresponding fiber bundle name, location information and Z-score value to generate a dynamic assessment report of the degree of fiber bundle abnormality; the dynamic assessment report lists the abnormality index, abnormality level, fiber bundle name, Z-score value and corresponding anatomical location of each fiber bundle. The abnormality index in the dynamic assessment report is mapped onto a three-dimensional brain template image; The generated dynamic assessment report and 3D brain template image are then transmitted to a visualization tool for clinical diagnosis and subsequent research. Storing dynamic assessment reports in a patient database and linking them to the patient's personal information and historical fiber bundle parameters provides data support for long-term tracking of the progression of white matter abnormalities in Parkinson's disease.

8. The method for automatic segmentation and quantitative labeling of fiber bundles for white matter abnormalities in Parkinson's disease according to claim 1, characterized in that, Also includes: Diffusion magnetic resonance imaging data collected at consecutive time points were extracted from the database of target Parkinson's disease patients, including fiber bundle parameter data at at least three time points; The fiber bundle parameter data at each time point are preprocessed to ensure that all data are unified to the same anatomical space. The TractSeg algorithm and fiber tracing algorithm in step S3 are used to calculate the fiber density, fiber cross-section and fiber density-cross-section product at each time point, respectively. The fiber bundle parameters at each time point were compared with the Z-score normalized database of the healthy control group, and the Z-score value of each fiber bundle parameter at each time point was calculated. Based on the Z-score, identify abnormal fiber bundles that exceed the preset threshold range at each time point, and record their name, anatomical location, and degree of abnormality. Time series analysis was performed on the Z-score values ​​of the same fiber bundle at different time points, and the rate of change of the Z-score values ​​was calculated. The rate of change was obtained by dividing the difference of the Z-score values ​​at adjacent time points by the time interval, and the unit was the monthly Z-score change. The dynamic trend of fiber bundle anomalies is determined based on the rate of change of Z-score values; The dynamic trend of each fiber bundle is combined with the corresponding Z-score value, fiber bundle name and anatomical location to generate a dynamic tracking report of fiber bundle abnormalities; The dynamic tracking report lists the Z-score value, rate of change, dynamic trend, and corresponding anatomical location of each fiber bundle at each time point, and displays the trend of Z-score value over time through a line graph. Based on the results of dynamic trend analysis, a predictive model for abnormal fiber bundles is generated. The predictive model is based on time series analysis and machine learning algorithms, combined with historical Z-score values ​​and rates of change, to predict the degree of abnormality of fiber bundles at a future time point. The prediction results are combined with the dynamic tracking report to generate a comprehensive trend analysis report, which includes the current degree of anomaly, historical trends and future predictions for each fiber bundle. The comprehensive trend analysis report is stored in the patient database and linked to the patient's personal information and historical fiber bundle parameters, providing data support for long-term dynamic monitoring and clinical intervention of white matter abnormalities in Parkinson's disease.

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