Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.770858
Title: Performance benchmarking of liver CT image segmentation and volume estimation
Authors: Xiong, W.
Zhou, J.
Tian, Q.
Liu, J.J.
Qi, Y.
Leow, W.K. 
Han, T.
Wang, S.-C.
Keywords: Abdominal CT imaging
CAD
Knowledge extraction
Liver segmentation
PACS
Performance benchmark
Probabilistic atlas
Issue Date: 2008
Source: Xiong, W., Zhou, J., Tian, Q., Liu, J.J., Qi, Y., Leow, W.K., Han, T., Wang, S.-C. (2008). Performance benchmarking of liver CT image segmentation and volume estimation. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 6919. ScholarBank@NUS Repository. https://doi.org/10.1117/12.770858
Abstract: In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Imagebased 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.
Source Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
URI: http://scholarbank.nus.edu.sg/handle/10635/41677
ISBN: 9780819471031
ISSN: 16057422
DOI: 10.1117/12.770858
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