Spatio-temporal deep learning methods for motion estimation using 4D OCT image data.

Journal: International journal of computer assisted radiology and surgery
PMID: 32445128

Abstract

PURPOSE: Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature-based methods.

Authors

  • Marcel Bengs
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
  • Nils Gessert
    Hamburg University of Technology, Schwarzenbergstraße 95 21073, Hamburg. Electronic address: mfbeg@sfu.ca.
  • Matthias Schlüter
    Hamburg University of Technology, Schwarzenbergstraße 95 21073, Hamburg.
  • Alexander Schlaefer
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. schlaefer@tuhh.de.

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