  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.
