In this paper, we propose a novel two-component loss for biomedical image
segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss,
a loss function that addresses the instance imbalance problem commonly
encountered when using pixel-wise loss functions such as the Dice loss. The
Instance-wise component improves the detection of small instances or ``blobs"
in image datasets with both large and small instances. The Center-of-Instance
component improves the overall detection accuracy. We compared the ICI loss
with two existing losses, the Dice loss and the blob loss, in the task of
stroke lesion segmentation using the ATLAS R2.0 challenge dataset from MICCAI
2022. Compared to the other losses, the ICI loss provided a better balanced
segmentation, and significantly outperformed the Dice loss with an improvement
of $1.7-3.7\%$ and the blob loss by $0.6-5.0\%$ in terms of the Dice similarity
coefficient on both validation and test set, suggesting that the ICI loss is a
potential solution to the instance imbalance problem.

本文提出了一种新的生物医学图像分割任务的两组分损失函数，称为实例和实例中心（ICI）损失，该损失函数解决了常常使用像素损失函数（如 Dice 损失）时遇到的实例不平衡问题。ICI 的实例部分改进了具有大型和小型实例的图像数据集中 “Blob” 的小实例检测。实例中心组件提高了整体检测精度。笔者将 ICI 损失与已有的 Dice 损失和 Blob 损失进行了比较，用于 MICCAI 2022 的 ATLAS R2.0 挑战数据集的卒中损伤分割任务。相对于其他损失，ICI 损失提供了更平衡的分割，并在验证集和测试集上的 Dice 相似系数方面比 Dice 损失提高了 1.7-3.7％，比 Blob 损失提高了 0.6-5.0％，这表明 ICI 损失是解决实例不平衡问题的潜在解决方案。