  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}.
