Segmentation of gallbladder from CT images for a surgical training system

Jiayin Zhou; Weimin Huang; Jing Zhang; Tao Yang; Jiang Liu; Chee Kong Chui; Stephen Chang

doi:10.1109/BMEI.2010.5639989

Segmentation of gallbladder from CT images for a surgical training system

Jiayin Zhou, Weimin Huang, Jing Zhang, Tao Yang, Jiang Liu, Chee Kong Chui, Stephen Chang

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract GB region from one single 2D slice in the intermediate part of a GB by voxel classification. Then the extracted GB contour, after some morphological operations, was projected to the neighboring slices for automated re-sampling, learning and further voxel classification in these slices. This propagation procedure continued till all GB-containing slices were processed. The method was tested using 18 CT data sets and a set of quantitative measures were computed. The averaged volume overlap error of 15.56% and surface distance of 0.64 mm suggested that the method is efficient and promising.

Original language	English
Title of host publication	Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Pages	536-540
Number of pages	5
DOIs	https://doi.org/10.1109/BMEI.2010.5639989
Publication status	Published - 2010
Externally published	Yes
Event	3rd International Conference on BioMedical Engineering and Informatics, BMEI 2010 - Yantai, China Duration: 16 Oct 2010 → 18 Oct 2010

Publication series

Name	Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Volume	2

Conference

Conference	3rd International Conference on BioMedical Engineering and Informatics, BMEI 2010
Country/Territory	China
City	Yantai
Period	16/10/10 → 18/10/10

Keywords

Gallbladder
Image segmentation
Image-guided surgical training
Support vector machine (SVM)

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.1109/BMEI.2010.5639989

Cite this

Zhou, J., Huang, W., Zhang, J., Yang, T., Liu, J., Chui, C. K., & Chang, S. (2010). Segmentation of gallbladder from CT images for a surgical training system. In Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010 (pp. 536-540). Article 5639989 (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010; Vol. 2). https://doi.org/10.1109/BMEI.2010.5639989

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title = "Segmentation of gallbladder from CT images for a surgical training system",

abstract = "A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract GB region from one single 2D slice in the intermediate part of a GB by voxel classification. Then the extracted GB contour, after some morphological operations, was projected to the neighboring slices for automated re-sampling, learning and further voxel classification in these slices. This propagation procedure continued till all GB-containing slices were processed. The method was tested using 18 CT data sets and a set of quantitative measures were computed. The averaged volume overlap error of 15.56% and surface distance of 0.64 mm suggested that the method is efficient and promising.",

keywords = "Gallbladder, Image segmentation, Image-guided surgical training, Support vector machine (SVM)",

author = "Jiayin Zhou and Weimin Huang and Jing Zhang and Tao Yang and Jiang Liu and Chui, {Chee Kong} and Stephen Chang",

note = "Copyright: Copyright 2011 Elsevier B.V., All rights reserved.; 3rd International Conference on BioMedical Engineering and Informatics, BMEI 2010 ; Conference date: 16-10-2010 Through 18-10-2010",

year = "2010",

doi = "10.1109/BMEI.2010.5639989",

language = "English",

isbn = "9781424464968",

series = "Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010",

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booktitle = "Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010",

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Zhou, J, Huang, W, Zhang, J, Yang, T, Liu, J, Chui, CK & Chang, S 2010, Segmentation of gallbladder from CT images for a surgical training system. in Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010., 5639989, Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010, vol. 2, pp. 536-540, 3rd International Conference on BioMedical Engineering and Informatics, BMEI 2010, Yantai, China, 16/10/10. https://doi.org/10.1109/BMEI.2010.5639989

Segmentation of gallbladder from CT images for a surgical training system. / Zhou, Jiayin; Huang, Weimin; Zhang, Jing et al.
Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010. 2010. p. 536-540 5639989 (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010; Vol. 2).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AU - Huang, Weimin

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AU - Yang, Tao

AU - Liu, Jiang

AU - Chui, Chee Kong

AU - Chang, Stephen

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AB - A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract GB region from one single 2D slice in the intermediate part of a GB by voxel classification. Then the extracted GB contour, after some morphological operations, was projected to the neighboring slices for automated re-sampling, learning and further voxel classification in these slices. This propagation procedure continued till all GB-containing slices were processed. The method was tested using 18 CT data sets and a set of quantitative measures were computed. The averaged volume overlap error of 15.56% and surface distance of 0.64 mm suggested that the method is efficient and promising.

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KW - Image-guided surgical training

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Zhou J, Huang W, Zhang J, Yang T, Liu J, Chui CK et al. Segmentation of gallbladder from CT images for a surgical training system. In Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010. 2010. p. 536-540. 5639989. (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010). doi: 10.1109/BMEI.2010.5639989

Segmentation of gallbladder from CT images for a surgical training system

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Keywords

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