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Updated: Jul 30, 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

Automatic multi-label temporal bone computed tomography segmentation with deep learning.

Langtao Zhou¹, Zhenhua Li²

¹School of Cyberspace Security, Guangzhou University, Guangzhou, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|May 19, 2023

Summary

This summary is machine-generated.

Deep learning accurately segments temporal bone structures from multi-scanner CT images. This automated approach addresses variations in imaging equipment for improved clinical applications.

Keywords:

automatic segmentation deep learning multi-label temporal bone

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Area of Science:

Medical Imaging
Artificial Intelligence
Anatomy

Background:

Manual segmentation of temporal bone CT scans is challenging.
Previous deep learning models overlooked scanner variations, impacting accuracy.
Scanner variability significantly affects segmentation performance.

Purpose of the Study:

To evaluate deep learning models for temporal bone segmentation across different CT scanners.
To assess the robustness of automated segmentation against clinical imaging variations.

Main Methods:

Utilized a dataset of 147 temporal bone CT scans from three distinct scanners.
Employed Res U-Net, SegResNet, and UNETR neural networks.
Segmented four key structures: ossicular chain (OC), internal auditory canal (IAC), facial nerve (FN), and labyrinth (LA).

Main Results:

Achieved high Dice similarity coefficients (e.g., 0.9329 for LA) and low Hausdorff distances (e.g., 0.0640 mm for LA).
Demonstrated successful segmentation across different scanners, indicating model robustness.
Quantified segmentation accuracy for OC, IAC, FN, and LA.

Conclusions:

Automated deep learning segmentation is effective for temporal bone structures using multi-scanner CT data.
The developed techniques show promise for enhanced clinical application in temporal bone imaging.
This study validates the feasibility of robust automated segmentation in diverse clinical settings.