Wang, C, Oda, M, Hayashi, Y et al. (4 more authors) (2020) Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation. Medical Image Analysis, 60. 101623. ISSN 1361-8415
Abstract
Blood vessel segmentation plays a fundamental role in many computer-aided diagnosis (CAD) systems, such as coronary artery stenosis quantification, cerebral aneurysm quantification, and retinal vascular tree analysis. Fine blood vessel segmentation can help build a more accurate computer-aided diagnosis system and help physicians gain a better understanding of vascular structures. The purpose of this article is to develop a blood vessel segmentation method that can improve segmentation accuracy in tiny blood vessels. In this work, we propose a tensor-based graph-cut method for blood vessel segmentation. With our method, each voxel can be modeled by a second-order tensor, allowing the capture of the intensity information and the geometric information for building a more accurate model for blood vessel segmentation. We compared our proposed method’s accuracy to several state-of-the-art blood vessel segmentation algorithms and performed experiments on both simulated and clinical CT datasets. Both experiments showed that our method achieved better state-of-the-art results than the competing techniques. The mean centerline overlap ratio of our proposed method is 84% on clinical CT data. Our proposed blood vessel segmentation method outperformed other state-of-the-art methods by 10% on clinical CT data. Tiny blood vessels in clinical CT data with a 1-mm radius can be extracted using the proposed technique. The experiments on a clinical dataset showed that the proposed method significantly improved the segmentation accuracy in tiny blood vessels.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Medical Image Analysis. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Blood vessel segmentation; Graph-cut; Renal artery; Tensor; Hessian matrix; Riemannian manifold |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 May 2020 14:15 |
Last Modified: | 01 Dec 2020 01:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2019.101623 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160607 |