Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking

verfasst von

Sontje Ihler, Felix Konstantin Kuhnke, Max-Heinrich Viktor Laves, Tobias Ortmaier

Abstract

Estimating tissue motion is crucial to provide automatic motion stabilization and guidance during surgery. However, endoscopic images often lack distinctive features and fine tissue deformation can only be captured with dense tracking methods like optical flow. To achieve high accuracy at high processing rates, we propose fine-tuning of a fast optical flow model to an unlabeled patient-specific image domain. We adopt multiple strategies to achieve unsupervised fine-tuning. First, we utilize a teacher-student approach to transfer knowledge from a slow but accurate teacher model to a fast student model. Secondly, we develop self-supervised tasks where the model is encouraged to learn from different but related examples. Comparisons with out-of-the-box models show that our method achieves significantly better results. Our experiments uncover the effects of different task combinations. We demonstrate that unsupervised fine-tuning can improve the performance of CNN-based tissue tracking and opens up a promising future direction.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Forschungszentrum L3S

Typ

Aufsatz in Konferenzband

Seiten

54-64

Anzahl der Seiten

11

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik, Informatik (insg.)

Elektronische Version(en)

https://doi.org/10.1007/978-3-030-59716-0_6 (Zugang: Geschlossen)