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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: May 12, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Intensity-Based Assessment of Hippocampal Segmentation Algorithms Using Paired Precontrast and Postcontrast MRI.

Justin Cramer¹, Leslie Baxter¹, Harrison Lang¹

¹Department of Radiology, Mayo Clinic Arizona, 5711 E Mayo Blvd, Phoenix, AZ 85054, USA.

Bioengineering (Basel, Switzerland)

|March 28, 2025

Summary

This summary is machine-generated.

Accurate hippocampal segmentation in MRI is crucial for diagnosing neurological conditions. This study introduces a novel voxel intensity method, finding that dedicated algorithms like HippMapp3r and e2dhipseg offer superior accuracy over whole-brain approaches.

Keywords:

Alzheimer’s dementia MRI volumetrics hippocampal segmentation mesial temporal sclerosis segmentation refinement

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Last Updated: May 12, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

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Published on: July 2, 2014

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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

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Published on: November 14, 2017

Area of Science:

Neuroimaging
Medical Image Analysis
Neurology

Background:

Hippocampal segmentation is vital for diagnosing neurological disorders like Alzheimer's dementia.
Inaccurate segmentation, often due to including cerebrospinal fluid (CSF) or choroid plexus, leads to overestimation and confounds analysis.
Existing assessment methods using virtual or manual labels may not detect these segmentation inaccuracies.

Purpose of the Study:

To develop and evaluate a direct voxel intensity-based method for assessing hippocampal segmentation accuracy.
To compare the performance of six different hippocampal segmentation algorithms.

Main Methods:

Utilized paired precontrast and postcontrast T1-weighted MRI scans.
Refined segmentations by adding marginal gray matter and removing marginal CSF and enhancement to calculate a total correction volume.
Assessed six algorithms: e2dhipseg, HippMapp3r, hippodeep, AssemblyNet, FastSurfer, and QuickNat.

Main Results:

HippMapp3r and e2dhipseg demonstrated the smallest total correction volumes, indicating higher accuracy.
hippodeep also showed competitive performance.
Dedicated hippocampal segmentation algorithms outperformed general whole-brain segmentation methods.

Conclusions:

The proposed voxel intensity-based method provides a more direct assessment of segmentation accuracy.
Specialized hippocampal segmentation algorithms are more accurate than whole-brain approaches for this region.
HippMapp3r and e2dhipseg are recommended for accurate hippocampal volumetry in neuroimaging studies.