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

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

Zixin Yang, Richard Simon, Cristian Linte

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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.

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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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