Ischemic region segmentation method and device, apparatus and storage medium

Abstract

The invention provides an ischemic region segmentation method and device, an apparatus and a storage medium. The method comprises the steps of obtaining a cerebral blood flow image of the brain of a target object; calculating a Z score graph corresponding to the cerebral blood flow image based on a predetermined brain image data set of the healthy people; and processing the cerebral blood flow image and the Z score graph by using a pre-trained classification model to obtain an ischemic region segmentation image of the brain of the target object. According to the ischemic region segmentation method, the ischemic region is segmented based on the statistical characteristics of the cerebral blood flow images, the information of individuals and the information compared with healthy groups are comprehensively considered, and the accuracy and robustness of ischemic region segmentation can be improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method, device, equipment and storage medium for ischemic region segmentation. Background technique [0002] Ischemic stroke is a common cerebrovascular disease, which has been seriously endangering human health, and its morbidity, disability and mortality are very high. How to quickly and effectively diagnose ischemic stroke is an important research topic in current clinical work. Early detection of ischemic stroke is very important, especially the correct detection in the hyperacute stage can guide timely measures to reduce brain cell necrosis in the ischemic penumbra area around the infarct. Diagnosis and treatment are of great importance. [0003] In the prior art, brain parenchyma imaging is commonly used for disease diagnosis, curative effect evaluation, and prognosis judgment, among which Arterial Spin Labeling (ASL) perfusion imaging technology is com...

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Problems solved by technology

[0004] However, because the existing technology for ischemic area segmentation based on CBF images only considers the information of the individual itself, and because the normal CBF value of some positions of the individual is relatively low, it is easy to cause segmentation errors, and this pass and contralateral value The judgment method of comparison will miss the possibility of bilateral ischemia, resulting in poor accuracy of segmentation results

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