Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images

Kawakami, Masashi; Hirata, Kenji; Furuya, Sho; Kobayashi, Kentaro; Sugimori, Hiroyuki; Magota, Keiichi; Katoh, Chietsugu

Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images

Kawakami, Masashi; Hirata, Kenji; Furuya, Sho et al.

Frontiers in medicine, 2020, 7, 616746

Permalink : https://hdl.handle.net/2115/80449

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アクセス権	open access
URI	https://hdl.handle.net/2115/80449
タイトル	Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images
著者	Kawakami, Masashi ; Hirata, Kenji ; Furuya, Sho ; Kobayashi, Kentaro ; Sugimori, Hiroyuki ; Magota, Keiichi ; Katoh, Chietsugu
著者名 Kawakami, Masashi
e-Rad_Researcher 30431365 著者名 Hirata, Kenji
著者名 Furuya, Sho
e-Rad_Researcher 70756311 著者名 Kobayashi, Kentaro
e-Rad_Researcher 20711899 著者名 Sugimori, Hiroyuki
e-Rad_Researcher 20636419 著者名 Magota, Keiichi
e-Rad_Researcher 10292012 著者名 Katoh, Chietsugu
言語	英語
キーワード	object detection
	deep learning
	positron emission tomography
	YOLOv2
	computer vision
発行日	2020-12-23
出版者	Frontiers Media
収録物名	Frontiers in medicine
巻(号)	7
ページ	616746
抄録	Deep learning technology is now used for medical imaging. YOLOv2 is an object detection model using deep learning. Here, we applied YOLOv2 to FDG-PET images to detect the physiological uptake on the images. We also investigated the detection precision of abnormal uptake by a combined technique with YOLOv2. Using 3,500 maximum intensity projection (MIP) images of 500 cases of whole-body FDG-PET examinations, we manually drew rectangular regions of interest with the size of each physiological uptake to create a dataset. Using YOLOv2, we performed image training as transfer learning by initial weight. We evaluated YOLOv2's physiological uptake detection by determining the intersection over union (IoU), average precision (AP), mean average precision (mAP), and frames per second (FPS). We also developed a combination method for detecting abnormal uptake by subtracting the YOLOv2-detected physiological uptake. We calculated the coverage rate, false-positive rate, and false-negative rate by comparing the combination method-generated color map with the abnormal findings identified by experienced radiologists. The APs for physiological uptakes were: brain, 0.993; liver, 0.913; and bladder, 0.879. The mAP was 0.831 for all classes with the IoU threshold value 0.5. Each subset's average FPS was 31.60 +/- 4.66. The combination method's coverage rate, false-positive rate, and false-negative rate for detecting abnormal uptake were 0.9205 +/- 0.0312, 0.3704 +/- 0.0213, and 0.1000 +/- 0.0774, respectively. The physiological uptake of FDG-PET on MIP images was quickly and precisely detected using YOLOv2. The combination method, which can be utilized the characteristics of the detector by YOLOv2, detected the radiologist-identified abnormalities with a high coverage rate. The detectability and fast response would thus be useful as a diagnostic tool.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
権利情報	Creative Commons Attribution 4.0 International
関連情報	PMID 33425962
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.3389/fmed.2020.616746