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Proceedings Paper

Performance benchmarking of liver CT image segmentation and volume estimation
Author(s): Wei Xiong; Jiayin Zhou; Qi Tian; Jimmy J. Liu; Yingyi Qi; Wee Kheng Leow; Thazin Han; Shih-chang Wang
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Paper Abstract

In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Image-based knowledge discovery plays important roles in many CAD applications, which have great potential to be integrated into the next-generation picture archiving and communication systems (PACS). Robust medical image segmentation tools are essentials for such discovery in many CAD applications. In this paper we present a platform with necessary tools for performance benchmarking for algorithms of liver segmentation and volume estimation used for liver transplantation planning. It includes an abdominal computer tomography (CT) image database (DB), annotation tools, a ground truth DB, and performance measure protocols. The proposed architecture is generic and can be used for other organs and imaging modalities. In the current study, approximately 70 sets of abdominal CT images with normal livers have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of organs, including 2D contours of liver, two kidneys, spleen, aorta and spinal canal. Abdominal organ segmentation algorithms using 2D atlases and 3D probabilistic atlases can be evaluated on the platform. Preliminary benchmark results from the liver segmentation algorithms which make use of statistical knowledge extracted from the abdominal CT image DB are also reported. We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built available to medical imaging research community for performance benchmarking of liver segmentation algorithms.

Paper Details

Date Published: 13 March 2008
PDF: 11 pages
Proc. SPIE 6919, Medical Imaging 2008: PACS and Imaging Informatics, 69190N (13 March 2008); doi: 10.1117/12.770858
Show Author Affiliations
Wei Xiong, Institute for Infocomm Research, A*STAR (Singapore)
Jiayin Zhou, School of Medicine, National Univ. of Singapore (Singapore)
Qi Tian, Institute for Infocomm Research, A*STAR (Singapore)
Jimmy J. Liu, Institute for Infocomm Research, A*STAR (Singapore)
Yingyi Qi, National Univ. of Singapore (Singapore)
Wee Kheng Leow, National Univ. of Singapore (Singapore)
Thazin Han, School of Medicine, National Univ. of Singapore (Singapore)
Shih-chang Wang, School of Medicine, National Univ. of Singapore (Singapore)


Published in SPIE Proceedings Vol. 6919:
Medical Imaging 2008: PACS and Imaging Informatics
Katherine P. Andriole; Khan M. Siddiqui, Editor(s)

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