Abstract: 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 regi

Abstract: The present disclosure provides an intracranial artery stenosis detection method and system. The present disclosure obtains artery stenosis detection results based on a first maximum intensity projection (MIP) image and a second MIP image obtained by preprocessing a medical image by adopting a detection model based on an adaptive triplet attention module and generates an auxiliary report and visualization results according to target category information in the artery stenosis detection results. Therefore, the problem that existing manual interpretation methods are easily affected by the subjective experience of doctors and are time-consuming and laborious can be solved, thus improving accuracy and efficiency of intracranial artery stenosis detection.