  Image Segmentation has been an active field of research as it has a wide
range of applications, ranging from automated disease detection to self-driving
cars. In the past five years, various papers came up with different objective
loss functions used in different cases such as biased data, sparse
segmentation, etc. In this paper, we have summarized some of the well-known
loss functions widely used for Image Segmentation and listed out the cases
where their usage can help in fast and better convergence of a model.
Furthermore, we have also introduced a new log-cosh dice loss function and
compared its performance on the NBFS skull-segmentation open-source data-set
with widely used loss functions. We also showcased that certain loss functions
perform well across all data-sets and can be taken as a good baseline choice in
unknown data distribution scenarios. Our code is available at Github:
https://github.com/shruti-jadon/Semantic-Segmentation-Loss-Functions.
