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This paper deals with segmentation of organs at risk (OAR) in head and neckarea in CT images which is a crucial step for reliable intensity modulatedradiotherapy treatment. We introduce a convolution neural network withencoder-decoder architecture and a new loss function, the batch soft Dice lossfunction, used to train the network. The resulting model produces segmentationsof every OAR in the public MICCAI 2015 Head And Neck Auto-SegmentationChallenge dataset. Despite the heavy class imbalance in the data, we improveaccuracy of current state-of-the-art methods by 0.33 mm in terms of averagesurface distance and by 0.11 in terms of Dice overlap coefficient on average.

Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss