Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Cochlea

The Cochlea

The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

CT imaging parameters of the oval window region can predict the extent of stapes footplate exposure in patients with otosclerosis.

Acta oto-laryngologica·2026

Same author

Multicenter Study of Multimodal MRI Radiomics and Deep Learning-Based Segmentation for Predicting Local Recurrence of Nasopharyngeal Carcinoma.

Cancers·2026

Same author

Targeted Epstein-Barr virus capture sequencing identifies BBLF4-L322M as an independent prognostic variant in nasopharyngeal carcinoma.

Microbiology spectrum·2026

Same author

Deep learning for discriminating cochlear malformations on temporal bone CT.

Brazilian journal of otorhinolaryngology·2026

Same author

Tree shrew immune cell atlas identifies NR1H3⁺ tissue macrophages with conserved anti-inflammatory function.

Nature communications·2026

Same author

Prognostic significance of quantitative EBV biomarkers in extranodal NK/T-cell lymphoma: a meta-analysis of EBV DNA load and EBER-positive cell proportion.

Infectious agents and cancer·2026

Search research articles

Related Experiment Video

Updated: May 31, 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

Utilizing deep learning for automatic segmentation of the cochleae in temporal bone computed tomography.

Zhenhua Li¹, Langtao Zhou², Songhua Tan³

¹Department of Otorhinolaryngology-Head and Neck Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, PR China.

Acta Radiologica (Stockholm, Sweden : 1987)

|January 22, 2025

Summary

Deep learning models can automatically segment cochleae in temporal bone CT scans, improving accuracy for otologic surgery. The SegResNet model demonstrated superior performance in differentiating normal and abnormal cochlear images.

Keywords:

Deep learning automatic segmentation cochlear malformation temporal bone computed tomography

More Related Videos

Extracting the Cochlea from a Human Temporal Bone: A Cadaveric Protocol

Extracting the Cochlea from a Human Temporal Bone: A Cadaveric Protocol

Published on: August 18, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Related Experiment Videos

Last Updated: May 31, 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

Extracting the Cochlea from a Human Temporal Bone: A Cadaveric Protocol

Extracting the Cochlea from a Human Temporal Bone: A Cadaveric Protocol

Published on: August 18, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Otolaryngology

Background:

Accurate segmentation of the cochlea in temporal bone computed tomography (CT) is crucial for image-guided otologic surgery.
Manual segmentation is a time-consuming and labor-intensive process, hindering clinical efficiency.

Purpose of the Study:

To evaluate the effectiveness of deep learning models for automated cochlear segmentation in temporal bone CT.
To assess the models' ability to distinguish between normal and abnormal cochlear images.

Main Methods:

Three deep learning models (3D U-Net, UNETR, SegResNet) were trained and tested on temporal bone CT datasets.
Datasets included normal and abnormal cochlear samples across different CT types (GE 64, GE 256, SE-DS).
Performance was evaluated using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD).

Main Results:

Model performance improved with the inclusion of abnormal cochlear images during training.
SegResNet achieved the highest performance, with an average DSC of 0.94 and HD of 0.16 mm on the test set.
Specific CT types showed slight variations in performance, with GE 256 CT yielding a DSC of 0.95 and SE-DS CT a DSC of 0.94.

Conclusions:

The SegResNet model is a feasible and accurate tool for automated cochlear segmentation in temporal bone CT.
Deep learning-based segmentation offers a more efficient alternative to manual methods for otologic surgery planning.