Liang, Y, Wang, F, Treanor, D et al. (4 more authors) (2015) A 3D Primary Vessel Reconstruction Framework with Serial Microscopy Images. In: Navab, N, Hornegger, J, Wells, WM and Frangi, AF, (eds.) MICCAI 2015 Part III. Lecture Notes in Computer Science. 18th International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI), 05-09 Oct 2015, Munich, Germany. Springer International , pp. 251-259. ISBN 978-3-319-24573-7
Abstract
Three dimensional microscopy images present significant potential to enhance biomedical studies. This paper presents an automated method for quantitative analysis of 3D primary vessel structures with histology whole slide images. With registered microscopy images, we identify primary vessels with an improved variational level set framework at each 2D slide. We propose a Vessel Directed Fitting Energy (VDFE) to provide prior information on vessel wall probability in an energy minimization paradigm. We find the optimal vessel cross-section associations along the image sequence with a two-stage procedure. Vessel mappings are first found between each pair of adjacent slides with a similarity function for four association cases. These bi-slide vessel components are further linked by Bayesian Maximum A Posteriori (MAP) estimation where the posterior probability is modeled as a Markov chain. The efficacy of the proposed method is demonstrated with 54 whole slide microscopy images of sequential sections from a human liver.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | (c) 2015, Springer International Publishing Switzerland. This is an author produced version of a paper published in MICCAI 2015 Lecture Notes in Computer Science. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24574-4_30 |
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: | 24 Aug 2015 08:37 |
Last Modified: | 19 Nov 2016 08:58 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-24574-4_30 |
Status: | Published |
Publisher: | Springer International |
Identification Number: | 10.1007/978-3-319-24574-4_30 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89128 |