Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort

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.

Keywords: Anatomical tracking; Cardiac MRI; Image-Guided radiation therapy; MR-LINAC; Self-supervised learning.