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

Frontiers in Medicine

article

Masashi Kawakami¹  Kenji Hirata²  Sho Furuya²  Kentaro Kobayashi²  Hiroyuki Sugimori³  Keiichi Magota⁴  Chietsugu Katoh¹ 

[1] Graduate School of Biomedical Science and Engineering, Hokkaido University;Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University;Faculty of Health Sciences, Hokkaido University;Division of Medical Imaging and Technology, Hokkaido University Hospital

关键词: object detection;    deep learning;    positron emission tomography;    YOLOv2;    computer vision;   

DOI  :  10.3389/fmed.2020.616746

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY