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

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by

Same author

A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imaging.

Scientific reports·2026

Same author

Multiple-TE Based Blood-Brain-Barrier Water Exchange Time Measurement Using a TE-Resolved 3D TSE Stack-Of-Spirals Readout.

Magnetic resonance in medicine·2026

Same author

Multimodal phenotypic classification of generalized anxiety and panic using structural MRI data and psychosocial factors: machine learning results from the German National Cohort (NAKO) study.

Translational psychiatry·2026

Same author

Multi-institutional classification of fibroglandular tissue and background parenchymal enhancement in breast MRI using deep learning.

Journal of medical imaging (Bellingham, Wash.)·2026

Same author

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.

European radiology·2026

Same author

Cerebral blood flow and time of exchange measurements using multi-TE blood-brain barrier arterial spin labeling MRI in IDH mutational subgroups of gliomas.

Magma (New York, N.Y.)·2026

Same journal

Online image reconstruction via Multiple Orthogonal Reference Sensitivity Encoding (MORSE).

Magma (New York, N.Y.)·2026

Same journal

Correction: MRS4Brain: a software for preclinical proton and deuterium-based MR spectroscopic imaging data.

Magma (New York, N.Y.)·2026

Same journal

Influence of gadolinium-based contrast agent (GBCA) on the diffusion weightings of breast lesions: an intra-patient analysis.

Magma (New York, N.Y.)·2026

Same journal

Evaluation of the diffusion time dependence of the IVIM effect based on realistic capillary flow simulations in mouse brain.

Magma (New York, N.Y.)·2026

Same journal

An evaluation of brain volume and cortical thickness measurement at 0.55 T.

Magma (New York, N.Y.)·2026

Same journal

Net zero emission MR imaging using a permanent 0.4 T magnet.

Magma (New York, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.

Lei Wang¹, Teodora Chitiboi², Hans Meine²

¹Institute for Medical Image Computing, Fraunhofer MEVIS Universitaetsallee 29, 28359, Bremen, Germany. lei.wang@mevis.fraunhofer.de.

Magma (New York, N.Y.)

|January 13, 2016

Summary

This summary is machine-generated.

This review covers magnetic resonance imaging (MRI) segmentation techniques for analyzing medical images. It discusses methods for superior tissue identification and addresses practical challenges in breast and cardiac MRI segmentation.

Keywords:

MRI Non-uniformity correction Segmentation

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Mar 27, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Image Processing
Biomedical Engineering

Background:

Magnetic Resonance Imaging (MRI) has generated vast amounts of image data.
Investigating specific tissues in 3D or 4D MR images requires advanced image processing.
Segmentation and registration are key techniques for MR image analysis.

Purpose of the Study:

To review principles and methods for superior tissue segmentation in MRI.
To discuss the impact of MR image acquisition on segmentation outcomes.
To explore practical challenges and solutions in MRI segmentation tasks.

Main Methods:

Review of common principles and methods for MRI tissue segmentation.
Analysis of MR image acquisition parameters influencing segmentation.
Discussion of segmentation technique selection for specific tissues.
Case studies of breast and myocardium segmentation in MRI.

Main Results:

Segmentation and registration techniques facilitate tissue investigation in MRI.
MR image acquisition significantly impacts segmentation results.
Tailored segmentation approaches are crucial for specific tissue identification.
Practical challenges in MRI segmentation include data variability and anatomical complexity.

Conclusions:

Effective MRI segmentation relies on understanding acquisition principles and choosing appropriate methods.
Addressing practical challenges requires tailored solutions for specific applications like breast and cardiac MRI.
This review provides insights into optimizing segmentation for accurate tissue analysis in MRI.