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Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy
Author(s): Gopichandh Danala; Yunzhi Wang; Theresa Thai; Camille C. Gunderson; Katherine M. Moxley; Kathleen Moore; Robert S. Mannel; Samuel Cheng; Hong Liu; Bin Zheng; Yuchen Qiu
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Paper Abstract

Accurate tumor segmentation is a critical step in the development of the computer-aided detection (CAD) based quantitative image analysis scheme for early stage prognostic evaluation of ovarian cancer patients. The purpose of this investigation is to assess the efficacy of several different methods to segment the metastatic tumors occurred in different organs of ovarian cancer patients. In this study, we developed a segmentation scheme consisting of eight different algorithms, which can be divided into three groups: 1) Region growth based methods; 2) Canny operator based methods; and 3) Partial differential equation (PDE) based methods. A number of 138 tumors acquired from 30 ovarian cancer patients were used to test the performance of these eight segmentation algorithms. The results demonstrate each of the tested tumors can be successfully segmented by at least one of the eight algorithms without the manual boundary correction. Furthermore, modified region growth, classical Canny detector, and fast marching, and threshold level set algorithms are suggested in the future development of the ovarian cancer related CAD schemes. This study may provide meaningful reference for developing novel quantitative image feature analysis scheme to more accurately predict the response of ovarian cancer patients to the chemotherapy at early stage.

Paper Details

Date Published: 20 February 2017
PDF: 6 pages
Proc. SPIE 10065, Biophotonics and Immune Responses XII, 100650J (20 February 2017); doi: 10.1117/12.2250978
Show Author Affiliations
Gopichandh Danala, The Univ. of Oklahoma (United States)
Yunzhi Wang, The Univ. of Oklahoma (United States)
Theresa Thai, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Camille C. Gunderson, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Katherine M. Moxley, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Kathleen Moore, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Robert S. Mannel, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Samuel Cheng, The Univ. of Oklahoma - Tulsa (United States)
Hong Liu, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)
Yuchen Qiu, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 10065:
Biophotonics and Immune Responses XII
Wei R. Chen, Editor(s)

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