A method for segmenting superficial cerebral veins in images

By segmenting superficial cerebral veins through magnetic susceptibility weighted imaging and deep learning models, the problem of insufficient research on superficial cerebral veins was solved, and quantitative assessment of superficial cerebral veins was achieved, providing a basis for clinical diagnosis and revealing the relationship between gender differences and lesions.

CN114742778BActive Publication Date: 2025-09-09GUANGZHOU INST OF SOFTWARE APPL TECH
2 Cites 0 Cited by

Patent Information

Application Number
CN202210331191.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-03-30
Publication Date
2025-09-09
Estimated Expiration
2042-03-30

AI Technical Summary

Technical Problem

There is little research on superficial cerebral veins in the existing technology, and there is a lack of effective image segmentation methods, which affects the evaluation and diagnosis of cerebrovascular diseases.

Method used

Susceptibility-weighted imaging technology combined with a deep learning model was used to segment and quantify superficial cerebral veins through the DeepLabV3+ network, including data preprocessing, model training and verification. The model was constructed using MinIP image data to achieve automatic quantification of superficial cerebral veins.

Benefits of technology

It provides an objective evaluation method that can quantify the morphological characteristics of superficial cerebral veins, providing a basis for the evaluation of clinical neurological diseases, and verifies the association between pathological changes in superficial cerebral veins and microinfarctions, and discovers gender differences and lesion distribution.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention discloses a method for segmenting superficial cerebral veins in an image, which belongs to the technical field of medical image processing and comprises the following steps: S1: selecting several healthy subjects; S2: performing conventional sequence scanning and susceptibility-weighted imaging; S3: SWI raw data processing; S4: constructing a data set; S5: data set preprocessing; S6: model training; S7: model application; S8: model extension application; the present invention combines the development advantages of susceptibility-weighted imaging, constructs a deep learning model based on MinIP images, and realizes preliminary quantitative analysis of the segmentation of morphological features of superficial cerebral veins, which can provide an objective reference method for the evaluation of superficial cerebral veins in clinical neurological diseases in the future. Based on this model, the hypothesis proposed by tissue anatomy that pathological changes of superficial cerebral veins can cause microinfarctions and thus lead to high signals in the white matter of the brain is discovered from an imaging perspective, and the gender difference in the distribution of the number of blood vessels in the superficial cerebral veins is discovered.
Need to check novelty before this filing date? Find Prior Art