  Semantic image segmentation, the process of classifying each pixel in an
image into a particular class, plays an important role in many visual
understanding systems. As the predominant criterion for evaluating the
performance of statistical models, loss functions are crucial for shaping the
development of deep learning-based segmentation algorithms and improving their
overall performance. To aid researchers in identifying the optimal loss
function for their particular application, this survey provides a comprehensive
and unified review of $25$ loss functions utilized in image segmentation. We
provide a novel taxonomy and thorough review of how these loss functions are
customized and leveraged in image segmentation, with a systematic
categorization emphasizing their significant features and applications.
Furthermore, to evaluate the efficacy of these methods in real-world scenarios,
we propose unbiased evaluations of some distinct and renowned loss functions on
established medical and natural image datasets. We conclude this review by
identifying current challenges and unveiling future research opportunities.
Finally, we have compiled the reviewed studies that have open-source
implementations on our GitHub page.
