Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-33612-6_19
Title: 3D shape analysis for liver-gallbladder anatomical structure retrieval
Authors: Huang, W.
Xiong, W.
Zhou, J.
Zhang, J.
Yang, T.
Liu, J.
Su, Y.
Lim, C.
Chui, C.K. 
Chang, S.
Keywords: Anatomical structure
shape analysis
shape comparison
surgical simulation
Issue Date: 2012
Source: Huang, W.,Xiong, W.,Zhou, J.,Zhang, J.,Yang, T.,Liu, J.,Su, Y.,Lim, C.,Chui, C.K.,Chang, S. (2012). 3D shape analysis for liver-gallbladder anatomical structure retrieval. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7601 LNCS : 178-187. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-33612-6_19
Abstract: Anatomical structure is important for medical education and disease diagnosis. In the application of surgical simulation, different anatomical structures can be retrieved to create variety of surgical scenarios for training, while similar structures can also be retrieved to assist disease diagnosis. This paper presents an approach to liver-gallbladder anatomical structure retrieval with 3D shape comparison, where the direct shape comparison based on dense shape registration is applied to liver shape due to its shape complexity, and feature based comparison is applied to gallbladder shape with a semantic shape decomposition using the saliency area based on multi-scale curvatures and concavity. After the registration of liver models, the geometric structure of the gallbladder and liver can be combined for joint comparison. With the 3D models constructed from a set of liver-gallbladder CT data, experiments are conducted for joint liver-gallbladder retrieval. Encouraging result shows that it can reveal important topology based on similarity and variance of 3D shapes and has a similar performance compared to that of manual retrieval by human operators. © 2012 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/72996
ISBN: 9783642336119
ISSN: 03029743
DOI: 10.1007/978-3-642-33612-6_19
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