Mask then classify: multi-instance segmentation for surgical instruments.

ABSTRACT:

Purpose

The detection and segmentation of surgical instruments has been a vital step for many applications in minimally invasive surgical robotics. Previously, the problem was tackled from a semantic segmentation perspective, yet these methods fail to provide good segmentation maps of instrument types and do not contain any information on the instance affiliation of each pixel. We propose to overcome this limitation by using a novel instance segmentation method which first masks instruments and then classifies them into their respective type.

Methods

We introduce a novel method for instance segmentation where a pixel-wise mask of each instance is found prior to classification. An encoder-decoder network is used to extract instrument instances, which are then separately class

SUBMITTER: Kurmann T 

PROVIDER: S-EPMC8260538 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature