2019/10/03 by Andréanne Lemay, Lemay, Andréanne
Computer Science · Engineering · Medicine · #Advanced Neural Network Applications #Artificial Intelligence in Healthcare and Education #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Organ Donation and Transplantation #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.01268
arxiv created 2019/10/03 · openalex publication_date 2019/10/03 · arxiv updated 2019/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Organ localization can be challenging considering the heterogeneity of medical images and the biological diversity from one individual to another. The contribution of this paper is to overview the performance of the object detection model, YOLOv3, on kidney localization in 2D and in 3D from CT scans. The model obtained a 0.851 Dice score in 2D and 0.742 in 3D. The SSD, a similar state-of-the-art object detection model, showed similar scores on the test set. YOLOv3 and SSD demonstrated the ability to detect kidneys on a wide variety of CT scans including patients suffering from different renal conditions.