  The analysis of animal cross section images, such as cross sections of
laboratory mice, is critical in assessing the effect of experimental drugs such
as the biodistribution of candidate compounds in preclinical drug development
stage. Tissue distribution of radiolabeled candidate therapeutic compounds can
be quantified using techniques like Quantitative Whole-Body Autoradiography
(QWBA).QWBA relies, among other aspects, on the accurate segmentation or
identification of key organs of interest in the animal cross section image such
as the brain, spine, heart, liver and others. We present a deep learning based
organ segmentation solution to this problem, using which we can achieve
automated organ segmentation with high precision (dice coefficient in the
0.83-0.95 range depending on organ) for the key organs of interest.
