Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy

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

doi:10.1117/12.2250978

20 February 2017 Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy

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

Author Affiliations +

Proceedings Volume 10065, Biophotonics and Immune Responses XII; 100650J (2017) https://doi.org/10.1117/12.2250978
Event: SPIE BiOS, 2017, San Francisco, California, United States

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.

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Gopichandh Danala, Yunzhi Wang, Theresa Thai, Camille C. Gunderson, Katherine M. Moxley, Kathleen Moore, Robert S. Mannel, Samuel Cheng, Hong Liu, Bin Zheng, and Yuchen Qiu "Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy", Proc. SPIE 10065, Biophotonics and Immune Responses XII, 100650J (20 February 2017); https://doi.org/10.1117/12.2250978

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