Semi-supervised aortic dissections segmentation: A time-dependent weighted feedback fusion framework

Abstract

The segmentation of true lumen (TL) and false lumen (FL) plays an important role in the diagnosis and treatment of aortic dissection (AD). Although the deep learning methods have achieved remarkable performance for this task, a large number of labeled data are required for training. In order to alleviate the burden of doctors' labeling, in this paper, a novel time-dependent weighted feedback fusion based semi-supervised aortic dissections segmentation framework is proposed by effectively leveraging the unlabeled data. A feedback network is additionally extended to encode the predicted output from the backbone network into high-level feature space, which is then fused with the original feature information of the image to fix previous potential mistakes, thereby segmentation accuracy is improved iteratively. To utilize both labeled data and unlabeled data, the fused feature space flows into the network again to generate the second feedback and make sure consistency with the previous one. The utilization of image feature space provides better robustness and accuracy for the proposed structure. Experiments show that our method outperforms five existing state-of-the-art semi-supervised segmentation methods on both a type-B AD dataset and a public dataset.

Keywords: Feature space consistency; Semi-supervised learning; Type-B AD segmentation.