  Deep convolutional neural networks (CNN) have proven to be remarkably
effective in semantic segmentation tasks. Most popular loss functions were
introduced targeting improved volumetric scores, such as the Dice coefficient
(DSC). 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 DSC.
Nevertheless, detecting tiny instances is crucial for many applications, such
as disease 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, \emph{blob loss}, primarily aimed at
maximizing instance-level detection metrics, such as F1 score and sensitivity.
\emph{Blob loss} is designed for semantic segmentation problems where detecting
multiple instances matters. We extensively evaluate a DSC-based \emph{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% improvement for MS lesions, 3% improvement for liver
tumor, and an average 2% improvement for microscopy segmentation tasks
considering F1 score.
