53 results about "Mri brain" patented technology

The invention provides an MRI brain tumor image segmentation method based on a state space model and a frequency domain, and relates to the field of image processing. The method comprises the following steps: acquiring a brain tumor MRI image to be segmented and preprocessing the brain tumor MRI image; and inputting the preprocessed brain tumor MRI image into a segmentation network based on a state space model and a frequency domain. The network is based on a U-Net architecture, and global context modeling is performed on feature information by introducing a state space model into a bottleneck layer. In order to optimize feature extraction, a dynamic weight mechanism is designed aiming at features input into a state space model, the importance of feature sequences in three directions is dynamically adjusted according to input data, and the attention to a brain tumor key area is enhanced. By adding a 3D frequency domain fusion module in jump connection, high-frequency noise is reduced, and perception of the overall structure of a brain tumor image is enhanced. According to the method, the state space model and the 3D frequency domain fusion module are introduced, and the importance of the feature sequence is dynamically adjusted, so that brain tumor MRI image segmentation with higher precision is realized.

The invention discloses a multi-axis RWKV-UNet + + multi-mode MRI (Magnetic Resonance Imaging) brain tumor segmentation method, and belongs to the technical field of medical image processing. According to the invention, multi-modal MRI three-dimensional body data is input and preprocessed, and fusion features are output through a modal fusion module; the fusion features are input into an encoder containing multi-axis RWKV sequence modeling, and long-range dependence is extracted; after the output of the encoder is processed by the bottleneck layer, the global Token aggregator converges the global context and reinjects the global context; the enhanced features are input into a UNet + + nested topology decoder, the jump features are fused with the up-sampling features after being subjected to jump RWKV semantic alignment, and finally a three-dimensional segmentation probability graph is generated through mapping. The method is mainly used for accurate three-dimensional segmentation of the multi-mode MRI brain tumor, and provides support for clinical brain tumor diagnosis and treatment.

The invention discloses a multi-modal MRI (Magnetic Resonance Imaging) brain tumor image segmentation method. The method comprises the following steps: data preprocessing and feature extraction: collecting and processing image data from different MRI sequences; adaptive feature selection and alignment: dynamically selecting the most representative modal features for alignment according to the information of the brain tumor; multi-scale feature fusion enhancement: introducing an extra network layer to perform feature fusion, the network layer being responsible for learning a weight distribution strategy, dynamically adjusting a fusion weight and strategy, inputting a fused feature map into a segmentation network, performing preliminary segmentation, and on the basis of the preliminary segmentation, performing multi-scale feature fusion enhancement; introducing an attention mechanism to guide the segmentation network to pay attention to the tumor region; reinforcement learning optimization: adopting a reinforcement learning algorithm to optimize the parameters and the structure of the segmentation network; uncertain region optimization: further optimizing the uncertain region in the segmentation result by adopting a post-processing method; and obtaining a three-dimensional tumor segmentation result: outputting the three-dimensional tumor segmentation result.

The invention provides a multi-modal MRI (Magnetic Resonance Imaging) brain tumor image segmentation method based on fusion of Transform and U-Net, belongs to the field of semantic segmentation, and aims to improve the speed and precision of MRI brain tumor image segmentation. The invention provides a novel method, existing knowledge and experience are utilized, Transform and U-Net models are fused and applied to an MRI brain tumor image segmentation task, an attention mechanism and an inverse residual module are introduced at the same time, and the model performance and generalization ability are remarkably improved. The MRI brain tumor image segmentation method is superior to a traditional segmentation method in the aspect of MRI brain tumor image segmentation, the accuracy of MRI brain tumor segmentation is improved, and important practical significance and guidance are provided for development of medical auxiliary diagnosis and treatment.

The invention discloses an improved multi-modal MRI (Magnetic Resonance Imaging) brain tumor segmentation method, which comprises the following steps of: firstly, acquiring a multi-modal MRI image containing a brain tumor, and preprocessing the multi-modal MRI image; secondly, based on the preprocessed multi-modal MRI image, enhanced modal correlation modeling and feature fusion are carried out, and smooth edge features are obtained; and finally, based on the smooth edge features, performing multi-task decoding and result output to obtain a complete brain tumor segmentation image. According to the method, the key problems of insufficient modal feature alignment, inter-task information segmentation, fuzzy segmentation boundary and the like in the existing multi-modal brain tumor segmentation are effectively solved.

The invention discloses a PET-based epilepsy postoperative prognosis information generation system and method, and the system comprises a preprocessing module, a target training data generation module, a training module and an application module which are sequentially connected. Through processing the whole brain health PET image, the preoperative whole brain PET image and the postoperative whole brain magnetic resonance imaging, the obtained target training data focuses on the metabolic connection characteristics of the focus area and the whole brain, a new view angle is provided for the postoperative prognosis prediction, so that the target training data is adopted to train the deep learning model, and the prognosis prediction accuracy is improved. A deep learning model focusing on the metabolic connection characteristics of the focus area and the whole brain can be obtained, so that the postoperative whole brain magnetic resonance imaging is accurately processed, and prognosis information is obtained.

The invention provides a fetal magnetic resonance image brain region segmentation method and system, computer equipment and a medium, and belongs to the technical field of automatic segmentation of fetal brain magnetic resonance imaging. The method comprises the steps that firstly, 2D low-resolution fetal magnetic resonance images collected in multiple directions are processed, and 3D high-resolution fetal brain images are generated; secondly, performing selective interlayer labeling on the 3D image, and generating a complete segmentation label through a contour interpolation algorithm; and finally, performing mutual supervision learning by using a double-independent initialized segmentation network, including the steps of data enhancement consistency constraint, cross pseudo label supervision, feature comparison learning and the like. According to the fetal magnetic resonance image brain region segmentation method and system, the computer equipment and the medium, a high-performance divider can be trained only through a very few labels, the labeling cost is remarkably reduced, the segmentation efficiency is improved, the method and system are suitable for actual clinical fetal brain magnetic resonance research, and efficient technical support is provided for fetal brain development evaluation.

The invention provides a scalp-to-brain magnetic resonance imaging generative model training method, a scalp-to-brain magnetic resonance imaging generative model generating method and scalp-to-brain magnetic resonance imaging generative model generating equipment, and relates to the technical field of image processing. The network comprises an encoder, a generator, a feature discriminator and an image discriminator, the encoder and / or the generator is integrated with a neural network model based on a frequency domain attention mechanism, and the encoder and the generator jointly form a target generation model after being trained; and the model parameters are jointly optimized by using a composite loss function comprising a frequency domain loss item. According to the method, the problems that an existing brain image generation technology neglects scalp structure information and is difficult to model global dependence and high-frequency details can be solved, brain magnetic resonance imaging data can be highly generated from scalp magnetic resonance imaging data, the structural integrity and topological fidelity of the generated data can be improved, and the application reliability of auxiliary diagnosis can be improved.

The invention discloses an automatic segmentation system and method based on a multi-mode MRI brain tumor image, and the system comprises an MCFE-Net network composed of an encoder and a decoder, and achieves the automatic segmentation of lesion regions WT, ET and TC of the MRI brain tumor image through the MCFE-Net network. A multi-convolution module is arranged in the middle layer of the encoder and the decoder, a cavity space receptive field module is arranged in the deepest layer of the encoder, and a modal feature attention module is arranged at the jump joint of the encoder and the decoder; wherein the multi-convolution module is used for improving the coding capability of the network while improving the width of the network; the cavity space receptive field module is used for fully utilizing the deep enriched semantic features and providing multi-scale context information so as to enhance the extraction of the network and utilize the deep semantic features; and the modal feature attention module is used for eliminating the influence of noise and irrelevant backgrounds and enhancing local expression. According to the method, the WT, ET and TC regions in the MRI brain tumor image can be efficiently segmented.

The invention discloses a multi-parameter MRI brain age prediction method and device based on uncertainty perception, equipment and a medium, and the method comprises the steps: determining a first feature sequence of an sMRI image, a second feature sequence of a DTI image, and a third feature sequence of an fMRI image through a feature extraction module; and determining a GM sampling vector, a DTI sampling vector and an fMRI sampling vector based on the first feature sequence, the second feature sequence and the third feature sequence by using a probability distribution encoder, and performing brain age prediction by using the GM sampling vector, the DTI sampling vector and the fMRI sampling vector. According to the method and the device, the fusion feature vector determined by fusing the sMRI, DTI and fMRI information is utilized to perform brain age prediction, so that multi-dimensional information of the brain structure and function can be more comprehensively acquired, and the accuracy of brain age prediction is improved. Meanwhile, a probability distribution encoder is introduced in the process of fusing the multi-parameter MRI images, multi-parameter MRI feature mapping is converted into Gaussian distribution through the probability distribution encoder to model feature uncertainty, richer potential information in the multi-parameter MRI images is captured, and the accuracy of brain age prediction is further improved.

A method for assessing a patient's brain disease state from brain images includes acquiring the brain images, partitioning them into predefined regions based on anatomical landmarks, extracting disease-indicative features, using a pretrained model to generate a disease-associated biomarker from the features, and determining the patient's brain disease state based on the biomarker.

The present application relates to a deformation registration system of MRI brain nerve images based on a double-flow cross network. The system comprises an acquisition module, a training module and an output module. The acquisition module is configured to acquire an MRI brain nerve image dataset. The training module is configured to input the dataset into a deformation image registration model based on a double-flow cross network, obtain a forward registration deformation field and a reverse registration deformation field, transform the obtained forward registration deformation field and reverse registration deformation field through a spatial transformation network, and generate a forward registration image and a reverse registration image. The output module is configured to output the forward registration image and the reverse registration image of two MRI brain nerve images to be deformation registered using the deformation image registration model learned in the training module. The system can automatically complete the deformation registration of any two 3D MR brain nerve images. When the images contain labels, brain atlas label migration can also be achieved, so that another image is automatically labeled.

The application relates to a brain tumor segmentation method and device based on TriSAM-UNet, which comprises the following steps: S1, collecting a multi-modal MRI brain tumor image dataset, and performing pretreatment and data enhancement; S2, constructing a hybrid segmentation network TriSAM-UNet, designing a hybrid module SAHM, capturing three-dimensional dependence and cross-region jump correlation, and realizing all-around global context perception; S3, constructing an FUE module, inhibiting fuzzy and noise response to ensure that high-confidence features are preferentially transmitted; S4, constructing a WFAB module, and restoring the boundary and texture details lost in up-sampling; and S5, training and optimizing the network model and performing full-automatic segmentation on the brain tumor image. The application aims to solve the problems of insufficient global context capture, indiscriminate forwarding of noise features in jump connection and loss of boundary details in up-sampling in brain tumor segmentation, and is suitable for the field of brain tumor image analysis.

The application discloses a 2.5D MRI brain tumor segmentation method based on Mamba state space modeling and belongs to the technical field of medical image segmentation. The application aims at the problems of the existing brain tumor segmentation method, such as long-range dependence modeling deficiency, high calculation cost, limited boundary segmentation precision and poor adaptability of the Mamba architecture to the 2.5D segmentation scene, and a cross-slice priority traversal module suitable for the 2.5D slice space structure is designed, local details and global semantic features are balanced through a feature calibration and controlled fusion module, and a global perception and edge enhancement dual-decoder structure is constructed to decouple tumor global modeling and boundary modeling tasks. The application reduces the calculation complexity while ensuring the segmentation precision, solves the problems of tumor segmentation discontinuity and boundary blur and is suitable for clinical conventional image workstations and has a good application prospect.

The invention discloses a PET / MRI (positron emission tomography / magnetic resonance imaging)-based A beta-PET semi-quantitative analysis method, which comprises the following steps of: acquiring a plurality of PET / MRI brain image samples and a plurality of PET / CT (computed tomography) brain image samples, and determining a reference area to calculate a first standard uptake value of the plurality of PET / MRI brain image samples and a second standard uptake value of the plurality of PET / CT brain image samples; performing linear regression analysis on the plurality of PET / MRI brain image samples and the plurality of PET / CT brain image samples to obtain calibration standard uptake values of the plurality of PET / MRI brain image samples; according to the method, the target area is determined, the Centiloid value of the PET / MRI brain image sample of the subject is calculated on the basis of the calibration standard uptake value to serve as the A beta-PET semi-quantitative analysis result, the problem that the evaluation of the A beta deposition level of the PET / MRI brain image is inaccurate is solved, and the accuracy of A beta-PET semi-quantitative analysis based on various PET / MRI scanning systems is improved.

The present invention belongs to the technical field of medical image processing and analysis, and relates to an MRI brain tumor segmentation method based on multi-modal feature fusion. An image segmentation model including an encoder and a decoder is constructed. In the encoder part, first, the input MRI images of four modalities, namely T1, T1c, T2, and Flair, are preliminarily feature-extracted through a standard convolution normalization activation module and a dilated convolution encoding enhancement module. Then, the extracted features are subjected to importance weighting and selection through a modality-level feature fusion module to obtain the fused features. In the decoder part, the feature representation is further extracted and optimized through a spatial and channel-level feature fusion module. Finally, the predicted segmentation image is output through an upsampling channel and spatial feature fusion convolution module. The present invention improves the accuracy of MRI brain tumor segmentation and provides strong technical support for clinical accurate diagnosis, treatment plan formulation, and patient monitoring.

The invention relates to a multi-modal MRI brain tumor segmentation method based on convolution attention, and the method comprises the steps: carrying out the parallel coding of multi-modal medical image data, and extracting the specific low-layer space features of each modal; performing feature fusion and high-level coding on the low-level spatial features to obtain shared features; carrying out convolution attention processing on the shared features to obtain enhanced features; and carrying out decoding processing on the enhanced features to obtain a segmentation result. According to the method, more accurate tumor boundary segmentation and tissue differentiation can be realized in medical image analysis such as brain MRI (Magnetic Resonance Imaging), meanwhile, the parameter efficiency, the calculation speed and the generalization ability under limited data of the model are remarkably improved, and the clinical practicability is enhanced.

The invention discloses a model, a method, an electronic device and a medium for MRI brain glioma segmentation, and the model comprises a learnable bias field correction module which is used for adaptively estimating and correcting a low-frequency bias field in an original brain glioma MRI image; the multi-resolution feature extraction module is used for carrying out multi-resolution feature extraction and outputting a multi-resolution feature graph group containing different resolution features and shallow layer features; the multi-resolution feature fusion module is used for carrying out feature fusion on the multi-resolution feature map group and recovering relatively high resolution to obtain deep features; and the statistical space gating fusion module is used for receiving the deep layer features and the shallow layer features, performing feature fusion through channel statistical screening and a space attention gating mechanism, and outputting an MRI brain glioma segmented image. The technical problems that an existing brain glioma MRI image segmentation method is generally poor in adaptability, insufficient in boundary continuity, low in fusion efficiency and poor in global and local feature balance are solved.

The invention relates to a multi-modal brain tumor segmentation method and device based on hierarchical attention enhancement, and the method comprises the following steps: S1, collecting a multi-modal MRI brain tumor image, constructing a data set, and carrying out the preprocessing; s2, constructing a multi-view region sensing feature aggregation module, performing feature extraction and attention enhancement on the brain tumor MRI, and fusing local features and global features; s3, constructing an adaptive correlation collaborative enhancement fusion module, focusing the key region, and carrying out adaptive fusion; s4, network parameters are updated through a category importance balance region perception composite loss function, and a network model is trained and adjusted to optimize segmentation performance; and S5, performing MRI brain tumor segmentation according to the trained segmentation model. According to the method, the multi-modal information can be effectively processed from the MRI image, the relation between different regions of the brain tumor is extracted and fused, and the brain tumor is accurately positioned and accurately segmented by combining the local features and the global features.

This invention provides a method, system, computer equipment, and medium for fetal magnetic resonance imaging (MRI) brain region segmentation, belonging to the field of automated segmentation technology for fetal brain MRI. The method includes: first, processing multi-planar acquired 2D low-resolution fetal MRI images to generate 3D high-resolution fetal brain images; second, performing selective inter-layer annotation on the 3D images and generating complete segmentation labels using a contour interpolation algorithm; and finally, using a dual-independent initialization segmentation network for mutual supervision learning, including steps such as data augmentation consistency constraints, cross-pseudo-label supervision, and feature comparison learning. This invention, employing the aforementioned fetal MRI brain region segmentation method, system, computer equipment, and medium, requires only a very small number of labels to train a high-performance segmenter, significantly reducing annotation costs and improving segmentation efficiency. It is suitable for practical clinical fetal brain MRI research, providing efficient technical support for fetal brain development assessment.

The invention provides a method for constructing a multi-sequence MRI (Magnetic Resonance Imaging) tumor diagnosis model, which comprises the following steps of: performing iterative training on a constructed MRI brain tumor diagnosis model: performing first-stage training: performing iterative training on the constructed MRI brain tumor diagnosis model by adopting a Focal loss function based on a first original sample set; after the first-stage training is completed, a difficult case list and an optimal MRI brain tumor diagnosis model are obtained; when the number of iterations of the first-stage training and / or the AUC index reach a corresponding preset threshold value, entering second-stage training; second-stage training: adding the obtained difficult case list to a second original sample set as a second-stage training sample set; based on the second-stage training sample set, performing iterative training on the optimal MRI brain tumor diagnosis model by adopting a joint loss function formed by Focal loss and Dice Loss until the model is converged; structural defects of single-round training and fixed matching are systematically solved, and more balanced optimization of the model among minority class recall, overall distinction degree and deployment threshold performance is achieved.

The present invention proposes a semi-supervised MRI brain tumor segmentation method based on a contrast-guided diffusion model, belonging to the field of image segmentation. In step S1, the T1 sequence data of the BraST2018 dataset is sliced, and the slice data without tumors is partially occluded; in step S2, pairs of lesion and healthy image data are generated, and the minimum bounding rectangle occlusion is constructed using the brain tumor sample mask, and the lesion area is restored to healthy tissue; in step S3, through the data pairs obtained in S2, the contrast information is used to guide the diffusion model to denoise and generate lesion labels. Thereafter, pre-training is performed with labeled data, and pseudo-labels are generated for unlabeled data through the pre-trained model, and then brought into the model together with the labeled data for the re-training process; in step S4, the structural contrast loss is used to improve the information mining ability of the model when the confidence of the pseudo-labels is insufficient; in step S5, only a small amount of labeled data is used for training and validation. The present invention realizes the lesion segmentation of MRI brain tumors by constructing a semi-supervised segmentation method based on a contrast-guided diffusion model, on the premise of only requiring a small amount of labeled data. This method solves the problem that traditional deep learning segmentation methods overly rely on a large amount of labeled data and improves the segmentation performance of the model under the condition of a small amount of labeled data.

The application discloses a brain glioma segmentation method based on CrossMAE, aiming at extracting visual language representation by combining MRI and diagnostic report to enhance segmentation performance. The method learns cross-modal deep correlation by randomly masking images and text information and reconstructing the masked content, thereby realizing accurate segmentation of MRI brain glioma. The method comprises the following steps: S1: collecting multi-modal MR brain tumor images and diagnostic reports, and constructing a data set; S2: designing a graphic-text mask encoder to selectively mask the data; S3: constructing a CrossMAE-based MRI brain glioma segmentation model; S4: designing a multi-task cascaded loss function, training and optimizing the segmentation model using the data set, and obtaining the trained MRI brain glioma segmentation model; S5: performing MRI brain glioma segmentation according to the trained MRI brain glioma segmentation model. The application effectively extracts and utilizes cross-modal complementary information from MRI, thereby realizing accurate segmentation of MRI brain glioma.

This invention discloses a method and system for neural fiber tract clustering and segmentation based on diffuse hypervoxels, relating to the fields of medical image analysis and artificial intelligence. The method includes: receiving and preprocessing three-dimensional brain magnetic resonance imaging (MRI) data; establishing a population-matched personalized MRI brain template using an iterative registration method; constructing a diffusion-weighted undirected graph and segmenting diffuse hypervoxels based on diffusion geodesic distance; clustering fiber tracts based on the maximum clusters of the neural fiber connectivity graph of the diffuse hypervoxels; registering the diffuse hypervoxels to an individual space and using the maximum clusters to achieve personalized fiber tract segmentation; constructing a center curve for the fiber tract clusters and mapping diffusion features to this curve; establishing a statistical analysis method for the center curve based on permutation tests and building a machine learning classification model.

The invention relates to a multi-modal MRI (Magnetic Resonance Imaging) brain tumor segmentation method based on teacher and student learning, which comprises the following steps: firstly, randomly selecting 80% as a training set and 20% as a test set from a brain tumor segmentation data set, the data comprising four modalities of Flair, T2, T1 and T1c, the Flair and T1c being teacher modalities, and the T2 and T1 being student modalities; then a segmentation network of a six-level encoder-decoder architecture is constructed, four encoders extract four modal features respectively, a teacher modal encoder comprises a standard convolution block, a cavity convolution block and a modal enhancement module, and a student modal encoder comprises a modal fusion module; the decoder comprises a cross-modal fusion module, a standard convolution block, a cavity convolution block, an up-sampling block and a depth supervision module; and finally, training the segmentation network by using the training set, inputting the test set into the trained network, and outputting a segmentation result by a decoder. The method provided by the invention can enhance the adaptability to tumor morphology and position changes, and the segmentation precision is high.

The invention provides an MRI (Magnetic Resonance Imaging) brain tumor image segmentation method based on Conv + Swin and CSWin Transform. Aiming at the defects of the existing MRI brain tumor image segmentation technology, the method is innovatively improved by taking TransUNet as a basic model. In an encoder, a mode that a CNN in a traditional TransUNet only uses a Conv structure is abandoned, a Conv + Swin combined mode is adopted, and SENetV2 is used as a backbone network for feature extraction, so that the capturing capability of image features is enhanced. Meanwhile, an original Transform encoder in an original model is replaced with a CSWin Transform structure, and self-attention is calculated in parallel on horizontal and vertical stripes, so that the attention area is effectively expanded, and the modeling capability of the model for global and local features is improved. Experiments prove that in the aspect of an MRI brain tumor image segmentation task, compared with a traditional TransUNet model, the segmentation precision of the improved model is remarkably improved, evaluation indexes such as a Dice coefficient are remarkably improved, a brain tumor area can be recognized and segmented more accurately, more reliable support is provided for medical diagnosis and treatment, and the method has wide application prospects in the field of medical image processing.

The application discloses a kind of multi-modal MRI brain tumor segmentation methods of cross-modal super-resolution guide, it is related to image segmentation technical field.The application is first by a multi-modal shared encoder to the four modal MRI of input is characterized learning, for T1 and T2 two low-resolution mode, independently designed super-resolution reconstruction branch is inputed with shared encoder output, using light-weight convolution-attention module is carried out global 2 times reconstruction, and 4 times fine enhancement is applied in candidate lesion area, super-reconstruction result is shared gradient with segmentation branch in optimization process, ensure that the high-frequency information generated meets segmentation semantics, segmentation branch uses decoder structure to predict tumor area, network is according to BraTS annotation standard and output three sub-regions: whole tumor (WT), tumor core (TC) and enhanced tumor (ET), introduce uncertainty estimator on the basis of segmentation output.This module is based on entropy estimation through shared encoder, ensure that super-resolution and segmentation task are consistent on bottom feature.

MRI BRAIN TUMOR IMAGE DETECTOR

The present invention discloses a zero-shot MRI brain tumor image generation method, belonging to the field of image generation. The method comprises the following steps: S1: acquiring and preprocessing data without a brain tumor sample; S2: constructing a brain tumor shape generation method; S3: constructing a brain tumor texture generation method; S4: constructing a method for simulating tumor mass effect and capsule effect; and S5: generating zero-shot MRI brain tumor images based on the aforementioned brain tumor shape generation method, texture generation method, and mass effect and capsule effect simulation methods. First, a lesion-free brain tumor dataset undergoes brain extraction and white matter segmentation. In step S2, a mathematical model is established to simulate tumor shape, generating core tumor regions, edema regions, and complete tumor regions. In step S3, texture simulation is performed using Gaussian noise and cubic spline interpolation, and Gaussian filtering is used to avoid oversharpening of the generated texture. Finally, in step S4, local scaling and warping are used to simulate tumor mass effect and capsule effect, respectively. This method generates zero-shot MRI brain tumor images using traditional image processing algorithms rather than deep neural networks, eliminating the need for tumor samples. The method increases the controllability of image generation, allowing precise adjustment of specific features in the image, such as tumor size, shape, and location.

The invention disclosed herein presents a method and system for individualized target localization for transcranial magnetic stimulation (TMS) in treating depression based on group-level differential statistical maps. The system acquires resting-state functional MRI (R-fMRI) brain imaging data from subjects in both a major depressive disorder (MDD) group and a matching normal control group, followed by data preprocessing. Taking the spherical subgenual anterior cingulate cortex (sgACC) as the seed point, functional connectivity calculations are performed for each subject, and sgACC functional connectivity maps within the mask of the dorsolateral prefrontal cortex (DLPFC) region are extracted. A two-sample t-test is conducted on the sgACC functional connectivity maps of the MDD and normal control groups to identify clusters within the DLPFC mask that show significant differences between the two groups, which are used as group-level localization targets. By integrating the obtained group-level localization targets with preprocessed individual MRI brain imaging data, individualized TMS targets are derived using a dual regression algorithm. This invention thoroughly considers the overall abnormal brain activities and individual functional variations of patients with depression, achieving precise individualized localization for TMS treatment in MDD patients.