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Abstract #0355

Self-supervised Training for Single-Shot Tumor Tracking in the Presence of Respiratory Motion

Marcel Früh1,2, Tobias Hepp1,3, Andreas Schilling4, Sergios Gatidis1,3, and Thomas Küstner1
1Medical Image And Data Analysis (MIDAS.lab), Department of Interventional and Diagnostic Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2University of Tuebingen, Tuebingen, Germany, 3Max Planck Institute for Intelligent Systems, Tuebingen, Germany, 4Department of Computer Science, Institute for Visual Computing, University of Tuebingen, Tuebingen, Germany


Real-time tumor tracking is a task of growing importance due to the increasing availability of modern linear accelerators paired with MR imaging, called MR-LINAC. Physiological motion can thereby impair focal treatment of moving lesions.Classical tracking approaches often work inadequately because they operate only at the pixel level and thus do not include image-level information.Contrarily, learning based tracking systems typically require a large, fully-annotated dataset which is an arduous task to create. In this work, we propose a framework for example-based single-shot tumor tracking, which is trained without presence of labels and investigated for lesion tracking under respiratory motion.

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