Brain arteriovenous malformation detection method and detection system based on CT images

Abstract

The invention discloses a brain arteriovenous malformation detection method and detection system based on CT images. The method comprises the steps of preprocessing the brain CT images to extract thebrain effective area; scanning the brain effective area on the basis of a three-dimensional convolution neural network algorithm, and automatically positioning all the lesion areas with brain arteriovenous malformation lesions; carrying out image segmentation on the edges of the lesion areas on the basis of the three-dimensional convolution neural network algorithm to automatically obtain lesion outline areas, and accurately distinguishing the lesions from normal brain tissue around; and automatically measuring the average density of the brain arteriovenous malformation lesions on the basis ofthe lesion outline areas. With the brain arteriovenous malformation detection method and detection system based on the CT images, the function of carrying out automatic positioning, edge automatic segmentation, average density automatic measurement and the like on the brain arteriovenous malformation lesion areas can be implemented; and furthermore, various image characteristics output when the detection is in use can be provided for doctors as the basis for determination so as to help the doctors do the clinical classification diagnosis work on the brain arteriovenous malformation lesions better.

Description

technical field [0001] The invention belongs to the technical field of medical image detection and relates to a medical image detection method based on artificial intelligence technology, in particular to a CT image-based brain arteriovenous malformation detection method and detection system. Background technique [0002] At present, radiologists and brain surgeons mostly diagnose brain arteriovenous malformations (AVM, hereinafter referred to as AVM) based on the following features in CT images, including lesion location, shape, size, internal structure, and the relationship with surrounding important nerve structures Adjacent relationship. Limited by the subjectivity of physicians' judgment standards and differences in training, it is difficult to accurately judge the grading of AVMs, especially micro AVMs, only with the naked eye. The onset of AVM usually causes cerebral hemorrhage, and patients often have symptoms such as severe headache and coma. This situation require...

AI Technical Summary

Problems solved by technology

Limited by the subjectivity and training of physicians, it is difficult to accurately judge the grading of AVMs with the naked eye, especially for tiny AVMs.

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