Pairwise learning for medical image segmentation.

Fully convolutional networks (FCNs) trained with abundant labeled data have been proven to be a powerful and efficient solution for medical image segmentation. However, FCNs often fail to achieve satisfactory results due to the lack of labelled data and significant variability of appearance in medical imaging. To address this challenging issue, this paper proposes a conjugate fully convolutional network (CFCN) where pairwise samples are input for capturing a rich context representation and guide each other with a fusion module. To avoid the overfitting problem introduced by intra-class heterogeneity and boundary ambiguity with a small number of training samples, we propose to explicitly exploit the prior information from the label space, termed as proxy supervision. We further extend the CFCN to a compact conjugate fully convolutional network (CFCN), which just has one head for fitting the proxy supervision without incurring two additional branches of decoders fitting ground truth of the input pairs compared to CFCN. In the test phase, the segmentation probability is inferred by the learned logical relation implied in the proxy supervision. Quantitative evaluation on the Liver Tumor Segmentation (LiTS) and Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) datasets shows that the proposed framework achieves a significant performance improvement on both binary segmentation and multi-category segmentation, especially with a limited amount of training data. The source code is available at https://github.com/renzhenwang/pairwise_segmentation.