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

You might also read

Related Articles

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

Sort by

Same author

Systemic Corticosteroids in the Management of Sinonasal Disease: An Evidence-Based Expert Review.

International forum of allergy & rhinology·2026

Same author

Towards controllable video synthesis of routine and rare OR events.

International journal of computer assisted radiology and surgery·2026

Same author

Evaluating the Efficacy of a Novel Multimaterial 3D-Printed Phantom for Otologic Surgical Education and Simulation.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology·2026

Same author

Legal Frameworks, Workforce Trends, and Collective Bargaining in Neurosurgery: Challenges and Models for the Future.

Neurosurgery·2026

Same author

Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026

Same author

TwinOR: photorealistic digital twins of dynamic operating rooms for embodied AI research.

International journal of computer assisted radiology and surgery·2026

Search research articles

Related Experiment Video

Updated: Jun 23, 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 Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

Manish Sahu¹, Yuliang Xiao¹, Jose L Porras²

¹Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, Maryland, USA.

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

|June 26, 2024

Summary

Deep learning segmentation of sinonasal structures in CT scans is efficient. A few labeled scans enable accurate automated segmentation, improving surgical planning and navigation.

Keywords:

automated segmentation and registration deep learning image‐guided surgery medical image segmentation semi‐supervised learning sinonasal computed tomography

More Related Videos

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jun 23, 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

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Area of Science:

Medical imaging and artificial intelligence
Radiology and otolaryngology

Background:

Manual segmentation of sinonasal structures in CT scans is time-consuming and resource-intensive.
Automated deep learning (DL) methods offer a potential solution to streamline this process.

Purpose of the Study:

To evaluate a label-efficient DL pipeline for semantic segmentation of sinonasal structures in CT scans.
To determine the minimum number of annotated scans required for accurate segmentation.

Main Methods:

A retrospective cohort study utilizing 40 CT scans.
A label-efficient DL framework trained on a mix of manually annotated and unlabeled scans.
Quantitative analysis to assess average surface distances (ASDs) based on the number of annotated scans.

Main Results:

Only four labeled scans were needed to achieve submillimeter ASDs for large sinonasal structures (nasal septum, inferior turbinate, maxillary sinus).
Eight labeled scans were required for accurate segmentation of smaller structures like the optic nerve.
The DL pipeline demonstrated high accuracy with minimal labeled data.

Conclusions:

A label-efficient DL pipeline can achieve submillimeter accuracy for sinonasal structure segmentation using a small number of labeled CT scans.
This automated approach has the potential to significantly enhance pre-operative planning, image-guided navigation, and computer-assisted diagnosis.