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Updated: Jul 5, 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

A deep learning-based interactive medical image segmentation framework with sequential memory.

Ivan Mikhailov¹, Benoit Chauveau², Nicolas Bourdel³

¹EnCoV, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, 63000, France; SurgAR, 22 All. Alan Turing, Clermont-Ferrand, 63000, France.

Computer Methods and Programs in Biomedicine

|January 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework for 3D medical image segmentation that leverages sequential user interactions. The new method significantly improves segmentation accuracy and reduces annotation time for complex medical imaging tasks.

Keywords:

CT Deep learning Interactive segmentation MRI RNN

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Last Updated: Jul 5, 2025

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Area of Science:

Medical Image Analysis
Deep Learning
Computer-Aided Diagnosis

Background:

3D medical image segmentation is crucial but challenging.
Existing interactive methods overlook the sequential nature of user corrections.
Exploiting interaction order can enhance segmentation performance.

Purpose of the Study:

To develop a deep learning framework for interactive 3D medical image segmentation.
To incorporate user interaction history (memory) into the segmentation process.
To improve accuracy and efficiency in medical image segmentation.

Main Methods:

A multi-class deep learning framework embedding a base network within a user interaction loop.
Explicitly modeling user feedback memory as a sequence of system states.
Utilizing a virtual user for dynamic simulation of iterative feedback during training.

Main Results:

The framework demonstrated superior performance in multi-class female pelvis MRI and liver/pancreas CT segmentation.
Achieved significant reduction in annotation time (5'56" vs. 25' for classical tools).
Outperformed existing automatic and interactive systems, especially for small and difficult-to-segment classes.

Conclusions:

The proposed framework offers substantial improvements in segmentation accuracy and efficiency.
Drastically reduces user segmentation time with fast inference speeds.
Highlights the importance of sequential user interaction data in deep learning for medical imaging.