Discriminative error prediction network for semi-supervised colon gland segmentation.

Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation directly affects the accuracy on the test set and the subsequent self-training with the error-corrected pseudo labels. In this paper, we propose a novel label rectification method based on error correction, namely ECLR, which can be directly added after the fully-supervised segmentation framework. Moreover, it can be used to guide the semi-supervised learning (SSL) process, constituting an error correction guided SSL framework, called ECGSSL. Specifically, we analyze the types and causes of segmentation error, and divide it into intra-class error and inter-class error caused by intra-class inconsistency and inter-class similarity problems in segmentation, respectively. Further, we propose a collaborative multi-task discriminative error prediction network (DEP-Net) to highlight two error types. For better training of DEP-Net, we propose specific mask degradation methods representing typical segmentation errors. Under the fully-supervised regime, the pre-trained DEP-Net is used to directly rectify the initial segmentation results of the test set. While, under the semi-supervised regime, a dual error correction method is proposed for unlabeled data to obtain more reliable network re-training. Our method is easy to apply to different segmentation models. Extensive experiments on gland segmentation verify that ECLR yields substantial improvements based on initial segmentation predictions. ECGSSL shows consistent improvements over a supervised baseline learned only from labeled data and achieves competitive performance compared with other popular semi-supervised methods.