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

Zixin Yang; Richard Simon; Cristian Linte

doi:10.1117/12.2610778

4 April 2022 A weakly supervised learning approach for surgical instrument segmentation from laparoscopic video sequences

Zixin Yang, Richard Simon, Cristian Linte

Author Affiliations +

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120341U (2022) https://doi.org/10.1117/12.2610778
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Citation Download Citation

Zixin Yang, Richard Simon, and Cristian Linte "A weakly supervised learning approach for surgical instrument segmentation from laparoscopic video sequences", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120341U (4 April 2022); https://doi.org/10.1117/12.2610778

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KEYWORDS

Image segmentation

Video

Machine learning

Binary data

Laparoscopy

Computer programming

Neural networks

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