• Pierre Jannin - Leader
• David Bouget - PhD student
• Laurent Riffaud - Medical expert

Detecting tools in surgical videos is an important indregient for context-aware computer-assisted intervention systems. We propose a new two-stage pipeline for tool detection and pose estimation in 2d images, named ShapeDetector. Our approach is data-driven and overcomes strong assumptions made regarding the geometry, number, and position of tools in the image. Our method has been validated for the following three pose parameters: overall position, tip location, and orientaton; using a new surgical tool dataset: the NeuroSurgicalTools data-set made of 2476 monocular images from neurosurgical microscopes during in-vivo surgeries.

**IMAGES AND ANNOTATIONS**

We provide separate train and test splits as long as corresponding annotations in the LabelMe format (one annotation file per image).

More info

• Rodrigo Benenson, Bernt Schiele, Max-Planck-Institut für Informatik, in Saarbrücken, Germany.
• Funding by Carl Zeiss, Germany