  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.
