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Updated: Jul 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Dissecting self-supervised learning methods for surgical computer vision.

Sanat Ramesh¹, Vinkle Srivastav², Deepak Alapatt²

¹ICube, University of Strasbourg, CNRS, Strasbourg 67000, France; Altair Robotics Lab, Department of Computer Science, University of Verona, Verona 37134, Italy.

Medical Image Analysis

|June 4, 2023

Summary

This summary is machine-generated.

Self-Supervised Learning (SSL) methods show promise for surgical computer vision by reducing annotation needs. Adapted SSL techniques significantly improve phase recognition and tool detection accuracy on surgical datasets.

Keywords:

Deep learning Endoscopic videos Laparoscopic cholecystectomy Self-supervised learning Semi-supervised learning Surgical computer vision

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Area of Science:

Computer Vision
Surgical Technology
Machine Learning

Background:

Deep learning models in surgical computer vision require extensive annotated data, which is costly and time-consuming to acquire.
Self-Supervised Learning (SSL) offers a way to train models on unlabeled data, potentially reducing annotation burdens.

Purpose of the Study:

To investigate the effectiveness of state-of-the-art SSL methods in the domain of surgical computer vision.
To analyze the performance of SSL methods for phase recognition and tool presence detection using the Cholec80 dataset.

Main Methods:

Evaluation of four SSL methods: MoCo v2, SimCLR, DINO, and SwAV.
Analysis of method parameterization and performance under varying training data quantities in semi-supervised settings.
Testing on the Cholec80 dataset for phase recognition and tool presence detection.

Main Results:

Adapted SSL methods achieved significant performance gains: up to 7.4% for phase recognition and 20% for tool presence detection compared to generic SSL.
SSL methods surpassed state-of-the-art semi-supervised approaches in phase recognition by up to 14%.
Demonstrated strong generalization capabilities across diverse surgical datasets.

Conclusions:

SSL methods, when appropriately transferred to surgical contexts, offer substantial improvements for computer vision tasks.
This research highlights the potential of SSL to overcome data annotation challenges in surgical AI.
The findings pave the way for more efficient and effective development of AI tools for surgery.