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논문 기본 정보

자료유형
학술대회자료
저자정보
Kouki Tsuji (Kyushu Institute of Technology) Joo Kooi Tan (Kyushu Institute of Technology) Hyoungseop Kim (Kyushu Institute of Technology) Kazue Yoneda (University of Occupational and Environmental Health) Fumihiro Tanaka (University of Occupational and Environmental Health)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2016
발행연도
2016.10
수록면
720 - 723 (4page)

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초록· 키워드

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Circulating tumor cells (CTCs) is an informative biomarker which assists pathologists in early diagnosis and evaluating therapeutic effects of patients with malignant tumors. The blood from a cancer patient is analyzed by a microscope and a large number of pictures including many cells are generated for each case. Thus, analyzing them is time-consuming work for pathologists, and misdiagnosis may happen since the diagnosis of CTCs tends to depend on the individual skill of pathologist. In this paper, we propose a method which detects cell candidate regions in microscopy images automatically to make quantitative analysis possible by computer. Our proposed method consists of three steps. In the first step, we extract initial cell candidate regions in microscopy images based on the saliency map. In the second step, we choose non-single cell regions from the initial candidates based on the SVM algorithm. In the third step, we separate connected regions into single cell regions based on the branch and bound algorithm. We demonstrated the effectiveness of our proposed method using 540 microscopy images and we achieved a true positive rate of 99.04[%] and a false positive rate of 3.95[%].

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Abstract
1. INTRODUCTION
2. METHODS
3. EXPERIMENT RESULTS
4. DISCUSSION AND CONCLUSIONS
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