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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...

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Updated: Jan 17, 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

Comprehensive Segmentation of Deep Grey Nuclei From Structural MRI Data.

Manojkumar Saranathan¹, Giuseppina Cogliandro², Thomas Hicks³

¹Department of Radiology, UMass Chan Medical School, Worcester, Massachusetts, USA.

Human Brain Mapping

|September 23, 2025

Summary

This summary is machine-generated.

A new method enables accurate segmentation of deep grey nuclei from standard MRI scans. This tool enhances reproducibility for deep grey nuclei research using readily available data.

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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

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Published on: February 4, 2022

Area of Science:

Neuroimaging
Medical Image Analysis
Radiology

Background:

Accurate segmentation of deep grey nuclei is crucial for neurological research.
Existing tools lack comprehensiveness, hindering reproducibility and repeatability.
Conventional T1 MRI data is widely available but underutilized due to segmentation limitations.

Purpose of the Study:

To develop a fast, accurate, and robust method for segmenting deep grey nuclei.
To enable segmentation using standard structural T1 MRI data across various field strengths.
To provide a single software solution for reproducible deep grey nuclei segmentation.

Main Methods:

Synthesized white-matter-nulled images from standard T1 MRI using Histogram-based Polynomial Synthesis (HIPS).
Employed multi-atlas segmentation with joint label fusion for deep grey nuclei segmentation.
Validated the method on 1.5T, 3T, and 7T MRI data.

Main Results:

Achieved Dice coefficients ≥ 0.7 for all segmented deep grey nuclei structures.
Demonstrated robustness across different MRI field strengths (1.5T, 3T, 7T).
The method proved effective compared to manual segmentation ground truth.

Conclusions:

The developed method offers a reliable approach for deep grey nuclei segmentation.
Facilitates the investigation of deep grey nuclei using conventional T1 MRI data from large databases.
Overcomes previous limitations in reproducible segmentation tools for deep grey nuclei.