A neural network approach to segment brain blood vessels in digital subtraction angiography. (March 2020)

• Record Type: Journal Article
• Authors: Zhang, Min; Zhang, Chen; Wu, Xian; Cao, Xinhua; Young, Geoffrey S.; Chen, Huai; Xu, Xiaoyin
• Is Part Of: Computer methods and programs in biomedicine. Volume 185(2020)
• Publication Date: 2020-03
• Subjects: Brain blood vessels -- Deep learning -- Digital subtraction angiography (DSA) -- Neural network -- Segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105159
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We presented a method to find blood vessels in digital subtraction angiography. The deep learning method can segment brain blood vessels of different diameters. Test results on real images show that the method achieved good performance. Abstract: Background and objective: Cerebrovascular diseases (CVDs) affect a large number of patients and often have devastating outcomes. The hallmarks of CVDs are the abnormalities formed on brain blood vessels, including protrusions, narrows, widening, and bifurcation of the blood vessels. CVDs are often diagnosed by digital subtraction angiography (DSA) yet the interpretation of DSA is challenging as one must carefully examine each brain blood vessel. The objective of this work is to develop a computerized analysis approach for automated segmentation of brain blood vessels. Methods: In this work, we present a U-net based deep learning approach, combined with pre-processing, to track and segment brain blood vessels in DSA images. We compared the results given by the deep learning approach with manually marked ground truth using accuracy, sensitivity, specificity, and Dice coefficient. Results: Our results showed that the proposed approach achieved an accuracy of 0.978, with a standard deviation of 0.00796, a sensitivity of 0.76 with a standard deviation of 0.096, a specificity of 0.994 with a standard deviation of 0.0036, and an average Dice coefficient was 0.8268 with a standard deviation of 0.052. Conclusions: Our findings show that the deep learning approach can achieve satisfactory performance as a computer-aided analysis tool to assist clinicians in diagnosing CVDs.