A weakly supervised learning approach for surgical instrument segmentation from laparoscopic video sequences

Abstract

Fully supervised learning approaches for surgical instrument segmentation from video images usually require a time-consuming process of generating accurate ground truth segmentation masks. We propose an alternative way of labeling surgical instruments for binary segmentation that first commences with rough, scribble-like annotations of the surgical instruments using a disc-shaped brush. We then present a framework that starts with a graph-model-based method for generating initial segmentation labels based on the user-annotated paint-brush scribbles and then proceeds with a deep learning model that learns from the noisy, initial segmentation labels. Experiments conducted on the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge have shown that the proposed framework achieved a 76.82% IoU and 85.70% Dice score on binary instrument segmentation. Based on these metrics, the proposed method out-performs other weakly supervised techniques and achieves a close performance to that achieved via fully supervised networks, but eliminates the need for ground truth segmentation masks.