ROBUST-MIS (Robust Medical Instrument Segmentation Challenge 2019)

The ROBUST-MIS dataset was made available to support the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019, part of the Endoscopic Vision Challenge associated with MICCAI.

The goal of this challenge is the benchmarking of algorithms for medical instrument detection and segmentation with a specific emphasis on robustness and generalization capabilities of the methods. The challenge is based on the biggest annotated data set made available in the field at the time, comprising 10,000 annotated images that have been extracted from a total of 30 surgical procedures from three different surgery types.

Data acquisition took place during daily routine procedures in: - rectal resection, - proctocolectomy and - UNKNOWN SURGERY (will be made public after the docker submission deadline)

surgeries in the Heidelberg University Hospital, Department of Surgery. The resulting laparoscopic video data was then anonymized by excluding parts of the video displaying parts outside the abdomen.

A training case encompasses a 10 second video snippet in form of 250 endoscopic image frames and a reference annotation for the last frame. In the annotated frame a “0” indicates the absence of a medical instrument and numbers “1”, “2“, ... represent different instances of medical instruments.

The test cases are identical in format but do not include a reference annotation.

UPDATE: While all training and test cases were used for the multiple instance detection task, cases not showing an instrument in the image were removed from training and test sets for the binary and multiple instance segmentation tasks.