Deep convolutional neural networks have proven to be remarkably effective in
semantic segmentation tasks. Most popular loss functions were introduced
targeting improved volumetric scores, such as the Sorensen Dice coefficient. By
design, DSC can tackle class imbalance; however, it does not recognize instance
imbalance within a class. As a result, a large foreground instance can dominate
minor instances and still produce a satisfactory Sorensen Dice coefficient.
Nevertheless, missing out on instances will lead to poor detection performance.
This represents a critical issue in applications such as disease progression
monitoring. For example, it is imperative to locate and surveil small-scale
lesions in the follow-up of multiple sclerosis patients. We propose a novel
family of loss functions, nicknamed blob loss, primarily aimed at maximizing
instance-level detection metrics, such as F1 score and sensitivity. Blob loss
is designed for semantic segmentation problems in which the instances are the
connected components within a class. We extensively evaluate a DSC-based blob
loss in five complex 3D semantic segmentation tasks featuring pronounced
instance heterogeneity in terms of texture and morphology. Compared to soft
Dice loss, we achieve 5 percent improvement for MS lesions, 3 percent
improvement for liver tumor, and an average 2 percent improvement for
Microscopy segmentation tasks considering F1 score.Comment: 23 pages, 7 figures // corrected one mistake where it said beta
instead of alpha in the tex