Distance Map Loss Penalty Term for Semantic Segmentation

TL;DR

This paper introduces a novel distance map-based loss penalty for CNNs in semantic segmentation, significantly improving boundary accuracy and shape preservation, especially in medical imaging tasks like 3D MRI bone segmentation.

Contribution

The paper proposes a new distance map-derived loss penalty that enhances boundary segmentation accuracy in CNNs, particularly for medical imaging applications.

Findings

Improved segmentation quality at object boundaries.

Enhanced shape preservation in segmentation results.

Better performance compared to traditional loss functions.

Abstract

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.