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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

Light mixed-supervised segmentation for 3D medical image data.

Hongxu Yang¹, Tao Tan¹, Pal Tegzes²

¹GE Healthcare, Eindhoven, The Netherlands.

Medical Physics

|November 1, 2023

Summary

This summary is machine-generated.

This study introduces a mixed-supervised learning method for 3D medical image segmentation, significantly reducing annotation effort. The approach achieves stable and accurate segmentation even with relaxed bounding box annotations, outperforming state-of-the-art methods.

Keywords:

3D medical images contrastive learning mixed-supervised learning relaxed bounding box

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Last Updated: Jul 12, 2025

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Published on: August 13, 2014

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Accurate 3D semantic segmentation is crucial for clinical applications.
Voxel-level annotation for 3D medical data is labor-intensive and raises privacy concerns.
Current slice-by-slice annotation methods are time-consuming.

Purpose of the Study:

To develop a 3D segmentation model that reduces annotation effort.
To overcome the limitations of existing weakly supervised methods that require tight bounding boxes.
To enable stable model training using relaxed bounding box annotations.

Main Methods:

A mixed-supervised training strategy is proposed for 3D segmentation.
Only one slice requires full contour annotation; others use relaxed bounding boxes.
The method integrates fully supervised learning, relaxed bounding box priors, and contrastive learning.

Main Results:

Achieved high segmentation Dice scores: 85.3% on MRI prostate and 83.3% on Vestibular Schwannoma datasets.
Outperformed state-of-the-art methods using relaxed bounding box annotations.
Demonstrated stable model performance despite variations in bounding box accuracy.

Conclusions:

A mixed-supervised learning method for 3D medical imaging is presented.
The approach allows for stable segmentation with reduced annotation accuracy requirements.
Facilitates easier model training on large-scale medical datasets.