Narrow-band loss - a novel loss function focused on target boundary

Loss functions widely employed in medical image segmentation, e.g., Dice or Generalized Dice, treat each pixel of segmentation target(s) equally. These region-based loss functions are concerned with the overall segmentation accuracy. However, in clinical applications, the focus of attention is often the boundary area of the target organ(s). Existing region-based loss functions lack attention to boundary areas. We designed narrow-band loss, which computes the integration of the predicted probability within the narrow-band around the target boundary. From the aspect of how it's derived, Narrow-band loss belongs to the region-based loss function. The difference from normal region-based loss is that Narrow-band loss calculates based on the degree of coincidence of the region surrounding the organ boundary. The advantage is that narrowband loss can guide networks to focus on the target's boundary and neighborhood. We also generalize narrow-band loss to multi-target segmentation. We tested narrow-band loss on two datasets of different parts of the human body: the brain dataset with 416 cases, each case with 35 labels, and the abdominal dataset with 50 cases, each case with 12 labels. Narrow-band loss has improved greatly in hd95 metric and dice metric compared with baseline, which is dice loss only.