Min Zhang1, Chen Zhang2, Xian Wu2, Xinhua Cao3, Geoffrey S Young1, Huai Chen4, Xiaoyin Xu5. 1. Departments of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. 2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. 3. Departments of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA. 4. Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China. 5. Departments of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. Electronic address: xxu@bwh.harvard.edu.
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.
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.
Authors: Marco Boegel; Philip Hoelter; Thomas Redel; Andreas Maier; Joachim Hornegger; Arnd Doerfler Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2015
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Authors: Artur Klepaczko; Piotr Szczypiński; Andreas Deistung; Jürgen R Reichenbach; Andrzej Materka Journal: Comput Methods Programs Biomed Date: 2016-10-06 Impact factor: 5.428