Segmentation Loss Odyssey

• URL: http://arxiv.org/abs/2005.13449v1
• Date: Wed, 27 May 2020 16:00:55 GMT
• Title: Segmentation Loss Odyssey
• Abstract summary: We present a systematic taxonomy to sort existing loss functions into four meaningful categories. We also explore the relationship between the traditional region-based and the more recent boundary-based loss functions.
• Score: 10.051935123214355
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods. Many loss functions have been proposed in existing literature, but are studied separately or only investigated with few other losses. In this paper, we present a systematic taxonomy to sort existing loss functions into four meaningful categories. This helps to reveal links and fundamental similarities between them. Moreover, we explore the relationship between the traditional region-based and the more recent boundary-based loss functions. The PyTorch implementations of these loss functions are publicly available at \url{https://github.com/JunMa11/SegLoss}.

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