Liang, Y, Wang, F, Treanor, D et al. (4 more authors) (2015) Liver whole slide image analysis for 3D vessel reconstruction. In: IEEE International Symposium on Biomedical Imaging. IEEE International Symposium on Biomedical Imaging, 16-19 Apr 2015, New York, USA. Institute of Electrical and Electronics Engineers , 182 - 185. ISBN 978-1-4799-2374-8
Abstract
The emergence of digital pathology has enabled numerous quantitative analyses of histopathology structures. However, most pathology image analyses are limited to two-dimensional datasets, resulting in substantial information loss and incomplete interpretation. To address this, we have developed a complete framework for three-dimensional whole slide image analysis and demonstrated its efficacy on 3D vessel structure analysis with liver tissue sections. The proposed workflow includes components on image registration, vessel segmentation, vessel cross-section association, object interpolation, and volumetric rendering. For 3D vessel reconstruction, a cost function is formulated based on shape descriptors, spatial similarity and trajectory smoothness by taking into account four vessel association scenarios. An efficient entropy-based Relaxed Integer Programming (eRIP) method is proposed to identify the optimal inter-frame vessel associations. The reconstructed 3D vessels are both quantitatively and qualitatively validated. Evaluation results demonstrate high efficiency and accuracy of the proposed method, suggesting its promise to support further 3D vessel analysis with whole slide images.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Keywords: | Whole Slide Image Analysis; 3D Vessel analysis; Vessel Reconstruction; Digital Pathology |
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) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 May 2015 13:59 |
Last Modified: | 17 Jan 2018 00:16 |
Published Version: | http://dx.doi.org/10.1109/ISBI.2015.7163845 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/ISBI.2015.7163845 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85780 |