  The segmentation of organs at risk (OAR) is a required precondition for the
cancer treatment with image guided radiation therapy. The automation of the
segmentation task is therefore of high clinical relevance. Deep Learning (DL)
based medical image segmentation is currently the most successful approach, but
suffers from the over-presence of the background class and the anatomically
given organ size difference, which is most severe in the head and neck (HAN)
area. To tackle the HAN area specific class imbalance problem we first optimize
the patch-size of the currently best performing general purpose segmentation
framework, the nnU-Net, based on the introduced class imbalance measurement,
and second, introduce the class adaptive Dice loss to further compensate for
the highly imbalanced setting. Both the patch-size and the loss function are
parameters with direct influence on the class imbalance and their optimization
leads to a 3\% increase of the Dice score and 22% reduction of the 95%
Hausdorff distance compared to the baseline, finally reaching $0.8\pm0.15$ and
$3.17\pm1.7$ mm for the segmentation of seven HAN organs using a single and
simple neural network. The patch-size optimization and the class adaptive Dice
loss are both simply integrable in current DL based segmentation approaches and
allow to increase the performance for class imbalanced segmentation tasks.
