28 citations · 36 across the 3 of their papers we have counts for
10 papers
Deep learning with 4D spatio-temporal data representations for OCT-based force estimation
Nils Gessert, Marcel Bengs, Matthias Schlüter +1
Estimating the forces acting between instruments and tissue is a challenging problem for robot-assisted minimally-invasive surgery. Recently, numerous vision-based methods have bee…
Deep Learning for High Speed Optical Coherence Elastography
Maximilian Neidhardt, Marcel Bengs, Sarah Latus +3
Mechanical properties of tissue provide valuable information for identifying lesions. One approach to obtain quantitative estimates of elastic properties is shear wave elastography…
Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data
Marcel Bengs, Nils Gessert, Matthias Schlüter +1
Purpose. Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography…
High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images
Matthias Schlüter, Lukas Glandorf, Johanna Sprenger +4
Modern optical coherence tomography (OCT) devices provide volumetric images with micrometer-scale spatial resolution and a temporal resolution beyond video rate. In this work, we a…
In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation
Florian Griese, Sarah Latus, Matthias Schlüter +4
Purpose: Using 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts…
Bimodal intravascular volumetric imaging combining OCT and MPI
Sarah Latus, Florian Griese, Matthias Schlüter +6
Intravascular optical coherence tomography (IVOCT) is a catheter based image modality allowing for high resolution imaging of vessels. It is based on a fast sequential acquisition…