Learning Agreement from Multi-source Annotations for Medical Image Segmentation

In medical image analysis, it is typical to merge multiple independent annotations as ground truth to mitigate the bias caused by individual annotation preference. However, arbitrating the final annotation is not always effective because new biases might be produced during the process, especially when there are significant variations among annotations. This paper proposes a novel Uncertainty-guided Multi-source Annotation Network (UMA-Net) to learn medical image segmentation directly from multiple annotations. UMA-Net consists of a UNet with two quality-specific predictors, an Annotation Uncertainty Estimation Module (AUEM) and a Quality Assessment Module (QAM). Specifically, AUEM estimates pixel-wise uncertainty maps of each annotation and encourages them to reach an agreement on reliable pixels/voxels. The uncertainty maps then guide the UNet to learn from the reliable pixels/voxels by weighting the segmentation loss. QAM grades the uncertainty maps into high-quality or low-quality groups based on assessment scores. The UNet is further implemented to contain a high-quality learning head (H-head) and a low-quality learning head (L-head). H-head purely learns with high-quality uncertainty maps to avoid error accumulation and keeps strong prediction ability, while L-head leverages the low-quality uncertainty maps to assist the backbone to learn maximum representation knowledge. UNet with H-head will be reserved during the inference stage, and the rest of the modules can be removed freely for computational efficiency. We conduct extensive experiments on an unsupervised 3D segmentation task and a supervised 2D segmentation task, respectively. The results show that our proposed UMA-Net outperforms state-of-the-art approaches, demonstrating its generality and effectiveness.