Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort. (October 2022)

• Record Type: Journal Article
• Authors: Frueh, Marcel; Kuestner, Thomas; Nachbar, Marcel; Thorwarth, Daniela; Schilling, Andreas; Gatidis, Sergios
• Is Part Of: Computer methods and programs in biomedicine. Volume 225(2022)
• Publication Date: 2022-10
• Subjects: Anatomical tracking -- Self-supervised learning -- MR-LINAC -- Image-Guided radiation therapy -- Cardiac MRI; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107085
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Self-Supervised learning (SSL) enables organ tracking with minimal labeling effort. SSL-based tracking yields superior results compared to conventional methods. SSL enables cross-subject anatomical matching. Abstract: Background and Objective: Tracking of anatomical structures in time-resolved medical image data plays an important role for various tasks such as volume change estimation or treatment planning. State-of-the-art deep learning techniques for automated tracking, while providing accurate results, require large amounts of human-labeled training data making their wide-spread use time- and resource-intensive. Our contribution in this work is the implementation and adaption of a self-supervised learning (SSL) framework that addresses this bottleneck of training data generation. Methods: To this end we adapted and implemented an SSL framework that allows for automated anatomical tracking without the necessity for human-labeled training data. We evaluated this method by comparison to conventional- and deep learning optical flow (OF)-based tracking methods. We applied all methods on three different time-resolved medical image datasets (abdominal MRI, cardiac MRI, and echocardiography) and assessed their accuracy regarding tracking of pre-defined anatomical structures within and across individuals. Results: We found that SSL-based tracking as well as OF-based methods provide accurate results for simple, rigid and smooth motion patterns. However, regarding more complex motion, e.g. non-rigid or discontinuous motion patterns in the cardiac region, and for cross-subject anatomical matching, SSL-based tracking showed markedly superior performance. Conclusion: We conclude that automated tracking of anatomical structures on time-resolved medical image data with minimal human labeling effort is feasible using SSL and can provide superior results compared to conventional and deep learning OF-based methods.