Intracranial hemorrhage parameter acquisition method and device based on self-supervised learning and M-Net

Abstract

The invention relates to an intracranial hemorrhage parameter acquisition method and device based on self-supervised learning and M-Net. Comprising the steps of obtaining brain CT sequence images, and preprocessing the brain CT sequence images, where the preprocessed brain CT sequence images at least comprise brain CT marked images and brain CT unmarked images, and the brain CT marked images are used for marking intracranial hemorrhage areas; inputting the brain CT unmarked image into a self-supervised learning network model for pre-training to obtain a first model; modifying model parameters of the first model based on the brain CT marker image data to obtain a second model; inputting the to-be-tested brain CT image into the second model, obtaining an intracranial hemorrhage area segmentation image, obtaining hemorrhage parameters of the to-be-tested brain CT image based on the segmentation image, where the hemorrhage parameters comprise a hemorrhage area and a hemorrhage area. According to the invention, a method of combining self-supervised learning and deep learning is adopted, and rapid identification of cerebral hemorrhage features and accurate calculation of the hemorrhage area are realized.

Description

technical field [0001] The present disclosure relates to the field of medical imaging, in particular to a method and device for acquiring intracranial hemorrhage parameters based on self-supervised learning and M-Net. Background technique [0002] Intracranial hemorrhage is a non-traumatic cerebral vascular disease in which blood vessels in the brain parenchyma rupture and blood directly enters the brain parenchyma or ventricles. It has the characteristics of high morbidity, high mortality, and high disability rate. There are many inducing factors, such as hypertension, arteriosclerosis, hyperglycemia, cerebral aneurysm, vascular malformation and so on. According to the World Health Organization, 12 to 15 out of every 100,000 people in the world suffer from intracranial hemorrhage. In my country, with the deepening of the aging process, intracranial hemorrhage has become a common acute cerebrovascular disease. According to the survey, patients with intracranial hemorrhage ...

AI Technical Summary

Problems solved by technology

However, at present, judging the location and amount of intracranial hemorrhage based on CT images not only requires clinicians to read and analyze a large amount of CT image data, which consumes a lot of time and energy for doctors and workers, but also the diagnostic results of clinicians with different experience levels may be different. lead to possible misdiagnosis or missed diagnosis

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