  Convolutional neural networks for semantic segmentation suffer from low
performance at object boundaries. In medical imaging, accurate representation
of tissue surfaces and volumes is important for tracking of disease biomarkers
such as tissue morphology and shape features. In this work, we propose a novel
distance map derived loss penalty term for semantic segmentation. We propose to
use distance maps, derived from ground truth masks, to create a penalty term,
guiding the network's focus towards hard-to-segment boundary regions. We
investigate the effects of this penalizing factor against cross-entropy, Dice,
and focal loss, among others, evaluating performance on a 3D MRI bone
segmentation task from the publicly available Osteoarthritis Initiative
dataset. We observe a significant improvement in the quality of segmentation,
with better shape preservation at bone boundaries and areas affected by partial
volume. We ultimately aim to use our loss penalty term to improve the
extraction of shape biomarkers and derive metrics to quantitatively evaluate
the preservation of shape.
