Real Time Landmark Detection for Within- and Cross Subject Tracking With Minimal Human Supervision

Landmark detection plays an important role for a variety of image processing and analysis tasks. Current methods rely on either supervised or semi-supervised learning which often requires large labeled training datasets. Also, retrospective addition of further target landmarks after completion of training is difficult in current methods. In this paper we propose a framework that addresses these limitations and allows for landmark detection based on only few examples and for definition of target landmarks after completed training without retraining. Our proposed approach relies on self-supervised training on a within-image template matching task with regularization by data augmentation. The trained network generalizes to cross-image matching and can thus be extended to example-based landmark detection and tracking. We extensively evaluate the proposed framework on chest X-ray images and abdominal MRI scans and demonstrate high accuracy with only few or even only one labeled example. Additionally we apply it to the task of liver and liver lesion tracking in CINE MRI scans.