Distance Map Loss Penalty Term for Semantic Segmentation

Francesco Caliva, Claudia Iriondo, Alejandro Morales Martinez, Sharmila Majumdar, Valentina Pedoia

Keywords: penalized loss, bone segmentation, distance maps, Osteoarthritis Initiative, magnetic resonance imaging, 3D convolutional neural networks

TL;DR: Distance transform based penalty loss term for accurate segmentation of object boundaries.

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.

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