Abstract
Accurate estimation of kidney volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. Existing kidney volume estimation methods rely on an intermediate segmentation step that is subject to various limitations. In this work, we propose a segmentation-free, supervised learning approach that addresses the challenges of accurate kidney volume estimation caused by extensive variations in kidney shape, size and orientation across subjects. We develop dual regression forests to simultaneously predict the kidney area per image slice, and kidney span per image volume. We validate our method on a dataset of 45 subjects with a total of 90 kidney samples. We obtained a volume estimation accuracy higher than existing segmentation-free (by 72 %) and segmentation-based methods (by 82%). Compared to a single regression model, the dual regression reduced the false positive area-estimates and improved volume estimation accuracy by 41 %. We also found a mean deviation of under 10% between our estimated kidney volumes and those obtained manually by expert radiologists.