Open AccessA weakly supervised learning approach for surgical instrument segmentation from laparoscopic video sequences
Author(s) -
Zixin Yang,
Richard Simon,
Cristian A. Linte
Publication year - 2022
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2610778
Subject(s) - segmentation , computer science , artificial intelligence , ground truth , computer vision , image segmentation , dice , supervised learning , graph , pattern recognition (psychology) , artificial neural network , mathematics , geometry , theoretical computer science
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.