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

Search research articles

Related Experiment Video

Updated: Mar 6, 2026

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

Deep Learning in Medical Image Analysis.

Dinggang Shen^1,2, Guorong Wu¹, Heung-Il Suk²

¹Department of Radiology, University of North Carolina, Chapel Hill, North Carolina 27599;

Annual Review of Biomedical Engineering

|March 17, 2017

Summary

This summary is machine-generated.

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

Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017

Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017

Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017

Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017

Same journal

Physics-Informed Machine Learning in Biomedical Science and Engineering.

Annual review of biomedical engineering·2026

Same journal

Advancements and Challenges in Computer-Assisted Medical Interventions for Image-Guided Prostate Cancer Treatments.

Annual review of biomedical engineering·2026

Same journal

Recent Advances in mRNA Therapeutic Cancer Vaccines.

Annual review of biomedical engineering·2026

Same journal

Artificial Intelligence-Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support.

Annual review of biomedical engineering·2026

Same journal

Viscoelasticity of the Heart: An Overview of Viscoelastic Measurements at Different Scales.

Annual review of biomedical engineering·2026

Same journal

Digital Twins for Biofluids.

Annual review of biomedical engineering·2026

See all related articles

Deep learning methods are revolutionizing medical image analysis by automatically identifying and quantifying patterns. This technology enhances disease diagnosis, prognosis, and various applications in computer-assisted medical imaging.

Area of Science:

Medical Imaging
Computer-Assisted Diagnosis
Machine Learning

Background:

Computer-assisted analysis of medical images is a rapidly evolving field.
Machine learning, particularly deep learning, offers advanced capabilities for pattern recognition in medical data.

Purpose of the Study:

To review recent advances in deep learning for medical image analysis.
To introduce the fundamentals of deep learning methods.
To discuss applications and future directions in the field.

Main Methods:

Review of literature on deep learning applications in medical imaging.
Explanation of hierarchical feature representations learned from data.
Focus on deep learning algorithms and their implementation.

Keywords:

deep learning medical image analysis unsupervised feature learning

Related Experiment Videos

Last Updated: Mar 6, 2026

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

Main Results:

Deep learning significantly enhances the identification, classification, and quantification of patterns in medical images.
State-of-the-art performance achieved in areas like image registration, structure detection, and segmentation.
Improved computer-aided diagnosis and prognosis through data-driven feature learning.

Conclusions:

Deep learning is transforming medical imaging analysis, surpassing traditional methods.
Further research is needed to address current challenges and explore future potential.
Continued development promises more accurate and efficient diagnostic tools.