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How loss function affects multiclass Deeplab segmentation of abdominal organs MRI

Loss function is the fundamental driver of backpropagation learning in deep convolutional neural networks (DCNN). There exist alternative formulations, such as cross entropy, jaccard and dice. But does the choice of loss influence quality decisively? Another relevant question is whether the quality of the best approaches nears perfection, as some seem to suggest. In this paper we investigate how variations of loss function affect the outcome of segmentation of abdominal organs by Magnetic resonance imaging (MRI). Variations include changing the loss function (cross entropy, dice, IoU), but also penalizing differently false positives and false negatives. We conclude that, for this problem of segmentation of abdominal organs, we were able to improve the quality of segmentation by 6% based on tuning the loss function on the best performing segmentation network (deeplabV3). The conclusions are important for anyone trying to segment these or other organs and structures. Future work is required to generalize the conclusions on other datasets as well, and to conclude in what concerns factors that determine the best choice.