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Related Experiment Video

Updated: Feb 25, 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

Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy.

Andrew J Plassard¹, Maureen McHugo², Stephan Heckers²

¹Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235.

Proceedings of Spie--The International Society for Optical Engineering

|August 8, 2017

Summary

This summary is machine-generated.

Automated segmentation of the hippocampus using multi-atlas methods significantly improves accuracy and speed. This new approach achieves high Dice similarity coefficients for hippocampus segmentation and sub-region delineation.

Keywords:

Hippocampus Multi-Atlas Segmentation

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Last Updated: Feb 25, 2026

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Published on: November 10, 2015

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Published on: February 4, 2022

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

The hippocampus plays a crucial role in memory.
Manual segmentation of the hippocampus is time-consuming (45 minutes per scan).
Automated and robust hippocampus segmentation methods are highly desired.

Purpose of the Study:

To develop an automated and robust method for hippocampus segmentation.
To accurately delineate the hippocampus into head and body segments.
To improve the speed and accuracy of hippocampus segmentation using multi-atlas techniques.

Main Methods:

Utilized a population of 195 atlases based on T1-weighted MR images.
Initialized multi-atlas segmentation to a region around the lateralized hippocampus.
Incorporated nearly 200 atlases for enhanced segmentation accuracy and computational efficiency.

Main Results:

Achieved a Dice similarity coefficient over 0.9 for full hippocampus segmentation.
Outperformed existing methods like BrainCOLOR (Dice 0.85) and FreeSurfer (Dice 0.75).
Delineation of head and body segments yielded Dice coefficients over 0.87.
Head and body volume measurements demonstrated high reproducibility (R² > 0.95).

Conclusions:

The proposed automated multi-atlas segmentation method is highly accurate and reproducible.
This technique significantly reduces computation time compared to traditional methods.
This work is foundational for developing tools for measuring the hippocampus and other temporal lobe structures.