  In this study, we explore quantitative correlates of qualitative human expert
perception. We discover that current quality metrics and loss functions,
considered for biomedical image segmentation tasks, correlate moderately with
segmentation quality assessment by experts, especially for small yet clinically
relevant structures, such as the enhancing tumor in brain glioma. We propose a
method employing classical statistics and experimental psychology to create
complementary compound loss functions for modern deep learning methods, towards
achieving a better fit with human quality assessment. When training a CNN for
delineating adult brain tumor in MR images, all four proposed loss candidates
outperform the established baselines on the clinically important and hardest to
segment enhancing tumor label, while maintaining performance for other label
channels.
