Distance Map Loss Penalty Term for Semantic Segmentation
This work addresses the challenge of accurate tissue surface and volume representation in medical imaging for tracking disease biomarkers, but it is incremental as it builds on existing loss functions like cross-entropy and Dice.
The paper tackles the problem of low performance at object boundaries in semantic segmentation for medical imaging by proposing a novel distance map derived loss penalty term, resulting in significant improvement in segmentation quality with better shape preservation at bone boundaries and areas affected by partial volume.
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.