Collaborative boundary-aware context encoding networks for error map prediction

Accurately assessing the medical image segmentation quality of the automatically generated predictions is essential for guaranteeing the reliability of the results of computer-assisted diagnosis (CAD). Many researchers have studied segmentation quality estimation without labeled ground truths. Recently, a novel idea is proposed, which transforms segmentation quality assessment (SQA) into the pixel-wise or voxel-wise error map segmentation task. However, the simple application of vanilla segmentation structures in medical domain fails to achieve satisfactory error segmentation results. In this paper, we propose collaborative boundary-aware context encoding networks called EP-Net for error segmentation task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks. Extensive experiments on IBSR V2.0 dataset, ACDC dataset and M&Ms dataset demonstrate that EP-Net achieves better error segmentation results compared with the traditional segmentation patterns. Based on error prediction results, we obtain a proxy metric of segmentation quality, which has high Pearson correlation coefficient with the real segmentation accuracy on all datasets.