  Medical image segmentation is crucial for clinical diagnosis. However,
current losses for medical image segmentation mainly focus on overall
segmentation results, with fewer losses proposed to guide boundary
segmentation. Those that do exist often need to be used in combination with
other losses and produce ineffective results. To address this issue, we have
developed a simple and effective loss called the Boundary Difference over Union
Loss (Boundary DoU Loss) to guide boundary region segmentation. It is obtained
by calculating the ratio of the difference set of prediction and ground truth
to the union of the difference set and the partial intersection set. Our loss
only relies on region calculation, making it easy to implement and training
stable without needing any additional losses. Additionally, we use the target
size to adaptively adjust attention applied to the boundary regions.
Experimental results using UNet, TransUNet, and Swin-UNet on two datasets (ACDC
and Synapse) demonstrate the effectiveness of our proposed loss function. Code
is available at https://github.com/sunfan-bvb/BoundaryDoULoss.
