CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery

Oluwatosin Alabi, Tom Vercauteren
Jun 3, 2025 1 min read
CholecInstanceSeg Github Read the Scientific Data paper

This paper introduces CholecInstanceSeg, the largest open-access dataset for surgical tool instance segmentation to date. It addresses key gaps in existing datasets by providing high-quality instance annotations for over 41,000 frames derived from clinical laparoscopic cholecystectomy procedures.

Visual representation of challenging annotation scenarios. Each image highlights a specific hard case: motion blur, smoke, soft tissue occlusion, tissue attachment, saturated lighting, instrument at the edge, reflection, instrument in fluid, low lighting, dirty lens, camera in port, and instrument far from the camera.
Visual representation of challenging annotation scenarios. Each image highlights a specific hard case: motion blur, smoke, soft tissue occlusion, tissue attachment, saturated lighting, instrument at the edge, reflection, instrument in fluid, low lighting, dirty lens, camera in port, and instrument far from the camera.
Oluwatosin Alabi
Oluwatosin Alabi
PhD Student

Tosin is a PhD student in teh EPSRC CDT in Smart Medical Imaging at King’s College London and Imperial College London, supervised by Dr Miaojing Shi and Prof Tom Vercauteren

Tom Vercauteren
Tom Vercauteren
Professor of Interventional Image Computing

Tom’s research interests include machine learning and computer assisted interventions