Dynamic Loss Weighting for Multiorgan Segmentation in Medical Images

Abstract

Deep neural networks often suffer from performance inconsistency for multiorgan segmentation in medical images; some organs are segmented far worse than others. The main reason might be organs with different levels of learning difficulty for segmentation mapping, due to variations such as size, texture complexity, shape irregularity, and imaging quality. In this article, we propose a principled class-reweighting algorithm, termed dynamic loss weighting, which dynamically assigns a larger loss weight to organs if they are discriminated as more difficult to learn according to the data and network's status, for forcing the network to learn from them more to maximally promote the performance consistency. This new algorithm uses an extra autoencoder to measure the discrepancy between the segmentation network's output and the ground truth and dynamically estimates the loss weight of organs per the contribution of the organ to the new updated discrepancy. It can capture the variation in organs' learning difficult during training, and it is neither sensitive to data's property nor dependent on human priors. We evaluate this algorithm in two multiorgan segmentation tasks: abdominal organs and head-neck structures, on publicly available datasets, with positive results obtained from extensive experiments which confirm the validity and effectiveness. Source codes are available at: https://github.com/YouyiSong/Dynamic-Loss-Weighting.