  Organ at risk (OAR) segmentation is a crucial step for treatment planning and
outcome determination in radiotherapy treatments of cancer patients. Several
deep learning based segmentation algorithms have been developed in recent
years, however, U-Net remains the de facto algorithm designed specifically for
biomedical image segmentation and has spawned many variants with known
weaknesses. In this study, our goal is to present simple architectural changes
in U-Net to improve its accuracy and generalization properties. Unlike many
other available studies evaluating their algorithms on single center data, we
thoroughly evaluate several variations of U-Net as well as our proposed
enhanced architecture on multiple data sets for an extensive and reliable study
of the OAR segmentation problem. Our enhanced segmentation model includes
(a)architectural changes in the loss function, (b)optimization framework, and
(c)convolution type. Testing on three publicly available multi-object
segmentation data sets, we achieved an average of 80% dice score compared to
the baseline U-Net performance of 63%.
