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Kazuki Hashimoto,Tohru Kamiya,Kazue Yoneda,Fumihiro Tanaka 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Today, cancer is the number one cause of death in Japan, and cancer accounts for 27.3% of all deaths number. The development of cancer by repeating metastasis, hence it is important to operate early detection and early treatment. The diagnosis of cancer includes various treatments, but it is difficult to judge whether cancer is metastatic or not. Then, analysis of Circulating Tumor Cells (CTCs) in blood has been attracting attention as a new biomarker. However, because the ratio of CTCs in a billion blood cells is only a few, and there is a concern that the burden on doctors will increase. We propose a method for automatic identification of CTCs from fluorescence microscopy images to enable quantitative analysis by computer for the diagnosis of CTCs in blood. First, after detecting the cell candidate regions mainly by filtering, we set the region of interest in the cell candidate regions and reconstruct the region of interest by cutting out the cell nucleus region. In this paper, we applied the proposed method to 5,040 images of 6 samples and conducted experiments on the identification of CTCs. As a result, the number of detections was 148( = 100%) and the number of over-detected non-CTCs was 988.
Kouki Tsuji,Joo Kooi Tan,Hyoungseop Kim,Kazue Yoneda,Fumihiro Tanaka 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
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[%].
Classification of Circulating Tumor Cells in Fluorescence Microscopy Images Based on SqueezeNet
Kazuki NAKAMICHI,Huimin LU,Hyoungseop KIM,Kazue YONEDA,Fumihiro TANAKA 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
Circulating Tumor Cells (CTC) is expected as a useful biomarker test that can evaluate cancer metastasis. CTC exists in the blood of cancer patients and is considered to be an incentive of cancer metastasis. Pathologists analyze the blood to find these metastasis cancers from three colors of fluorescence microscopy images, but the manual analysis is time-consuming. In this paper, we develop an automatic CTC classification method in fluorescence microscopy images to reduce the burden of pathologists. In the proposed method, we detect cell regions by the bacterial foraging-based edge detection (BFED) algorithm and classify CTC by SqueezeNet, which is the kind of convolutional neural network (CNN). We apply the proposed method to 5040 microscopy images (6 samples) and evaluate the effectiveness. The experimental results demonstrate that the proposed method has a true positive rate is 89.86% and a false positive rate is 3.27%.
Kouki Tsuji,Huimin Lu,Joo Kooi Tan,Hyoungseop Kim,Kazue Yoneda,Fumihiro Tanaka 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
Circulating tumor cells (CTCs) is a useful biomarker for cancer metastasis. The blood from a cancer patient is analyzed by a fluorescence microscope. Each case takes a large number of images, which usually have a lot of cell regions. Thus, analyzing the images is hard work for pathologists, and misdiagnosis may happen. In this paper, we develop an automatic CTCs identification method for fluorescence microscopy images. The proposed method consists of three steps. First, we extract cell regions in images using filtering methods. Second, we compute features of each CTC candidate regions. Finally, we identify the CTCs using AdaBoost algorithm. And we analyze the features to know which ones are effective for characterizing CTCs and normal cells. We apply the proposed method to 5040 microscopy images, and evaluate the effectiveness of our method by using leave-one-out cross validation. We achieve a true positive rate of 97.30 [%] and a false positive rate of 12.82 [%].
Malignant Melanoma of the Nipple: A Case Report
Yoshika Nagata,Manabu Yoshioka,Hidetaka Uramoto,Yosuke Tsurudome,Sohsuke Yamada,Takeshi Hanagiri,Fumihiro Tanaka 한국유방암학회 2018 Journal of breast cancer Vol.21 No.1
Malignant melanoma rarely originates from the female nipple. Tumors that develop on the skin of the breast are often subject to a delayed diagnosis. Cytologic examination provides excellent diagnostic capabilities and is a safe procedure with a lower risk of local implantation, compared to needle or incisional biopsy. We herein report a patient who underwent surgical resection of a primary malignant melanoma of the nipple. An elastic soft nodule was observed on the left nipple, and no abnormal lesions were identified in the breast. Eventually, a malignant melanoma was diagnosed from the clinical and cytological evaluation findings. This bulky tumor was classified as a stage IIIC nodular melanoma, with a thickness of 12 mm. The patient received adjuvant chemotherapy and exhibits no evidence of recurrence 7 years after surgery.