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FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation.

Luca Sestini¹, Benoit Rosa², Elena De Momi³

¹ICube, University of Strasbourg, CNRS, IHU Strasbourg, France; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.

Medical Image Analysis

|January 30, 2023

Summary

This study introduces FUN-SIS, a fully unsupervised method for surgical instrument segmentation in endoscopic images. It achieves performance comparable to supervised methods by using motion and shape priors on unlabeled videos.

Keywords:

Deep learning Endoscopy Generative adversarial network Learning from noisy labels Surgical instrument segmentation Unsupervised learning

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Area of Science:

Computer Vision
Medical Imaging
Robotic Surgery

Background:

Surgical instrument segmentation is vital for computer-assisted minimally invasive surgery.
Current methods require extensive manual annotation, which is costly and time-consuming.

Purpose of the Study:

To develop a fully unsupervised approach for surgical instrument segmentation.
To reduce reliance on manually annotated data for training segmentation models.

Main Methods:

FUN-SIS utilizes implicit motion information and instrument shape-priors from unlabeled endoscopic videos.
A generative-adversarial approach segments optical-flow images, creating pseudo-labels.
A learning-from-noisy-labels architecture refines these pseudo-labels for training.

Main Results:

The fully unsupervised FUN-SIS method achieves results nearly on par with fully supervised state-of-the-art approaches.
Validation was performed on three surgical datasets, including the MICCAI 2017 EndoVis dataset.

Conclusions:

FUN-SIS demonstrates significant potential for leveraging large amounts of unlabeled endoscopic video data.
This unsupervised approach can substantially decrease the cost and effort associated with surgical instrument segmentation model development.