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.

翻译：医学图像分割对于临床诊断至关重要。然而，当前医学图像分割的损失函数主要关注整体分割结果，针对边界分割提出的损失函数较少。即便存在此类损失函数，也常常需要与其他损失函数组合使用，且效果不佳。为解决这一问题，我们开发了一种简单而有效的损失函数——边界差异与并集比损失（Boundary DoU Loss），用于指导边界区域的分割。该损失函数通过计算预测分割与真实标注的差异集与差异集和部分交集并集的比值得到。我们的损失函数仅依赖区域计算，易于实现且训练稳定，无需额外损失函数辅助。此外，我们利用目标尺寸自适应调整对边界区域的注意力。在ACDC和Synapse两个数据集上使用UNet、TransUNet和Swin-UNet的实验结果表明了所提损失函数的有效性。代码获取地址：https://github.com/sunfan-bvb/BoundaryDoULoss。