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Related Experiment Video

Updated: Jun 9, 2026

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

Automatic data-driven real-time segmentation and recognition of surgical workflow.

Olga Dergachyova^1,2, David Bouget^3,4, Arnaud Huaulmé^3,4,5

¹INSERM, U1099, Rennes, 35000, France. olga.dergachyova@univ-rennes1.fr.

International Journal of Computer Assisted Radiology and Surgery

|March 21, 2016

Summary

This study introduces a data-driven method for real-time surgical phase recognition using video and instrument data. Combining these signals improves segmentation and accuracy in surgical workflow detection.

Keywords:

AdaBoost Computer-assisted surgery Hidden semi-Markov Model Surgical Process Modelling Surgical workflow

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Last Updated: Jun 9, 2026

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Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Published on: November 28, 2025

Area of Science:

Computer Vision
Surgical Robotics
Medical Informatics

Background:

Context-aware systems are increasingly important in operating rooms for enhanced surgical staff perception and action.
Accurate surgical workflow identification is crucial for these systems, requiring diverse sensor data.

Purpose of the Study:

To propose a fully data-driven, real-time method for segmenting and recognizing surgical phases.
To utilize a combination of video data and instrument usage signals without prior knowledge.
To introduce novel validation metrics for assessing workflow detection.

Main Methods:

A four-stage process involving automatic Surgical Process Model construction from annotations.
Data description using combined low-level visual cues and instrument information.
Training AdaBoost classifiers and using Hidden semi-Markov Models for phase recognition.

Main Results:

Achieved 91% precision and recall in classifying 7 phases on the MICCAI EndoVis challenge laparoscopic dataset.
Demonstrated superior performance compared to single-data-type analyses.

Conclusions:

Combining visual features and instrument signals leads to improved segmentation and reduced detection delay.
This integrated approach facilitates more accurate discovery of the correct surgical phase order.