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Updated: May 3, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation.

Caio A Neves^1,2, Trishia El Chemaly¹, Fanrui Fu¹

¹Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, California, USA.

Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery

|May 21, 2024

Summary

This summary is machine-generated.

A deep learning algorithm accurately segments temporal bone structures in CT scans, achieving high precision. This automated method shows promise for enhancing surgical planning and guidance in otologic procedures.

Keywords:

computed tomography convolutional neural networks deep learning image segmentation otologic surgery

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Last Updated: May 3, 2026

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

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Anatomy

Background:

Accurate segmentation of temporal bone structures is crucial for otologic surgery.
Manual segmentation is time-consuming and prone to variability.

Purpose of the Study:

To develop and validate a deep learning (DL) algorithm for automated segmentation of key temporal bone structures.
To assess the performance of the DL model using objective metrics.

Main Methods:

A SwinUNETR deep learning algorithm was trained on 325 clinical computed tomography (CT) scans.
Manual annotations by a specialist served as ground truth.
The model was validated on a test set using Dice coefficient, balanced accuracy, and Hausdorff distances.

Main Results:

The DL model achieved an average Dice coefficient of 0.87 and balanced accuracy of 0.94.
Average processing time was 9.1 seconds per CT scan.
Average Hausdorff distance was 0.79 mm.

Conclusions:

The developed DL model accurately segments multiple temporal bone structures from CT data.
This automated approach has the potential to improve preoperative evaluation and intraoperative guidance in otologic surgery.