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2019

Distance Map Loss Penalty Term for Semantic Segmentation

Caliva, Francesco, Iriondo, Claudia, Martinez, Alejandro Morales et al.

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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.

Distance Map Loss Penalty Term for Semantic Segmentation · Around

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The mathworks
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U-net: Convolutional networks for biomedical image segmentation
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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