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

Black Hole Spectroscopy and Tests of General Relativity with GW250114.

Physical review letters·2026

Same author

GW250114: Testing Hawking's Area Law and the Kerr Nature of Black Holes.

Physical review letters·2025

Same author

Characterising high-resolution dynamics of inflammatory and SCFA responses to food consumption in healthy men: A pilot biomarker discovery study.

Cytokine·2025

Same author

Agreement between early-phase amyloid-PET and pulsed arterial spin labeling in a memory clinic cohort.

Journal of molecular medicine (Berlin, Germany)·2025

Same author

Chemical Space Networks Enhance Toxicity Recognition via Graph Embedding.

Journal of chemical information and modeling·2025

Same author

Progress in the Treatment of Alzheimer's Disease Is Needed - Position Statement of European Alzheimer's Disease Consortium (EADC) Investigators.

The journal of prevention of Alzheimer's disease·2024

Search research articles

Related Experiment Video

Updated: Mar 31, 2026

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI Harmonized Hippocampal

P Inglese¹, N Amoroso¹, M Boccardi²

¹Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy; Università degli Studi di Bari, Bari, Italy.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)

|October 21, 2015

Summary

This study introduces an automated method using multiple classifiers to segment the hippocampus in MRI scans for neurodegenerative disease research. The approach accurately measures hippocampal changes, aiding in diagnosis.

Keywords:

Alzheimer's disease Hippocampus segmentation Random forest classifier

More Related Videos

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

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

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

Published on: November 14, 2017

Related Experiment Videos

Last Updated: Mar 31, 2026

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

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

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

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

Published on: November 14, 2017

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

The hippocampus is crucial in neurodegenerative diseases like Alzheimer's Disease.
Accurate segmentation of the hippocampus is vital for diagnosing and monitoring these conditions.

Purpose of the Study:

To develop and validate a novel, automated method for hippocampus segmentation from structural MRI.
To assess the method's robustness and reliability across different subject groups.

Main Methods:

A fully automated pipeline involving affine registration, bounding box extraction, and slice-by-slice Random Forest (RF) classification.
Fusion of segmentations from three orthogonal directions for improved accuracy.
Validation on T1 MRI scans from healthy controls, mild cognitive impairment, and Alzheimer's Disease subjects.

Main Results:

The multiple RF classifier approach achieved Dice coefficients of 0.87 ± 0.03, outperforming single RF methods.
The method demonstrated robustness and reliability on an external cohort.
Preliminary analysis showed local hippocampal morphology differences between subject groups.

Conclusions:

A multiple classification approach is effective for automated hippocampus segmentation.
This method supports accurate measurement of hippocampal volume and shape changes for diagnostic purposes.
The developed pipeline offers a reliable tool for neurodegenerative disease research.