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20172024
most citedSimultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

88 citations · 145 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

Online 3D reconstruction and dense tracking in endoscopic videos

Michel Hayoz, Christopher Hahne, Thomas Kurmann +5

3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene r…

cs.CV2019

Fused Detection of Retinal Biomarkers in OCT Volumes

Thomas Kurmann, Pablo Márquez-Neila, Siqing Yu +3

Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In…

cs.CV2019

Deep Multi Label Classification in Affine Subspaces

Thomas Kurmann, Pablo Marquez Neila, Sebastian Wolf +1

Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations f…

cs.CV201957 cited

2017 Robotic Instrument Segmentation Challenge

Max Allan, Alex Shvets, Thomas Kurmann +16

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand t…

cs.CV2018

Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery

Sebastian Bodenstedt, Max Allan, Anthony Agustinos +17

Intraoperative segmentation and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware like tracking sys…

cs.CV201788 cited

Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

Thomas Kurmann, Pablo Marquez Neila, Xiaofei Du +4

Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognitio…