Beihang University

Beijing Tiantan Hospital, Capital Medical University

Lingling Ding

Tao Liu

Yongjun Wang

Zixiao Li

Hao Liu

Shuo Zhang

Yanhui Li

Pan Liu

Ziyang Liu

Jing Jing

A method and system for extracting a multi-dimensional disconnection network region of symptom mapping: registering a lesion image to a brain standard space; obtaining diffusion magnetic resonance images and resting-state functional magnetic resonance images of healthy control groups; constructing a structural disconnection weighting network corresponding to lesions using a fiber tracking method according to the lesion image in the brain standard space and the diffusion magnetic resonance images; constructing a functional significant disconnection network corresponding to the lesions using a cross-correlation verification method according to the lesion image in the brain standard space and the resting-state functional magnetic resonance images; and determining the multi-dimensional disconnection network region of the lesions of symptom mapping according to the structural disconnection weighting network and the functional significant disconnection network, where the multi-dimensional disconnection network region of the lesions is configured to locate network mapping of a brain lesion in the brain.

Method and system for extracting multi-dimensional disconnection network region of symptom mapping

A method and system for processing an intracranial large vessel image, an electronic device, and a medium are provided. The method includes: acquiring original intracranial large vessel images of a target to be identified and a sample subject; applying a cerebrovascular segmentation model to obtain cerebrovascular mask images; computing regions of interest of the cerebrovascular mask images and bounding boxes; selecting corresponding regions of interest from the original intracranial large vessel images; separately preprocessing the mask image regions of interest and the original image regions of interest to obtain images to be processed; annotating a target region in the image to be processed of the sample subject; training a convolutional neural network model with a training set to obtain a cerebrovascular occlusion classification model; and inputting the image to be processed of the target to be identified to the cerebrovascular occlusion classification model to obtain a target region identification result.

Method and system for processing intracranial large vessel image, electronic device, and medium

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