Segmentation of robotic instruments is an important problem for robotic assisted minimially invasive surgery. It can be used for simple 2D applications such as overlay masking or 2D tracking but also for more complex 3D tasks such as pose estimation. In this challenge we invite applicants to participate in 3 different tasks: binary segmentation, multi-label segmentation and instrument recognition. Binary segmentation involves just separating the image into instruments and background, whereas multi-label segmentation requires the user to also recognize which parts of the instrument body correspond to the different articulated parts of a da Vinci robotic instrument. The final recogition task tests whether the user can recognize which segmentation corresponds to which da Vinci instrument type.

To achieve this we are providing 8x 225-frame robotic surgical videos, captured at 2 Hz, where a trained team at Intuitive Surgical has manually labelled the different parts and types. The users are invited to test their algorithms on 8x 75-frame videos and 2x 300-frame videos which act as a test set.

Description from: Robotic Instrument Segmentation Sub-Challenge