The paper introduces a multiple decoder U-net architecture for improved Organ at Risk (OAR) segmentation in CT images, crucial for radiation treatment planning. It uses segmentation disagreement between decoders as an attention mechanism and incorporates a CT intensity integrated regularization loss to handle low contrast tissues. Tested on two public OAR challenge datasets, the proposed methods significantly enhance segmentation accuracy.
