Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-supervised Mediastinal Segmentation in CT

Segmentation of the mediastinal vasculature in computed tomography (CT) enables automated extraction of important biomarkers for cardiopulmonary disease characterization and outcome prediction. However, the limited contrast between blood and surrounding soft tissue makes manual segmentation of mediastinal structures challenging in non-contrast CT (NCCT) images, resulting in limited annotations for training deep learning models. To overcome this challenge, we propose a semi-supervised mediastinal vasculature segmentation method that utilizes knowledge distillation from unlabeled training data of contrast-enhanced dual-energy CT to achieve segmentation of the main pulmonary artery, main pulmonary veins, and aorta in NCCT. Our framework incorporates multitask learning with attention feature fusion bridges for online knowledge transfer from a related image-to-image translation task to the target segmentation task. Experimental evaluations demonstrate superior segmentation accuracy of our approach compared to fully supervised methods as well as two sequential approaches that do not leverage distillation between tasks. The proposed approach achieves a Dice similarity coefficient of 0.871 for the main pulmonary artery, 0.920 for the aorta, and 0.824 for the main pulmonary veins. By leveraging a large dataset without annotations through multitask learning and knowledge distillation, our approach improves performance in the target task of mediastinal segmentation with limited annotated training data.