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Related Experiment Video

Updated: Sep 10, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation.

Chiara Mauri^1,2, Ryan Fritz¹, Jocelyn Mora¹

¹Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Human Brain Mapping

|August 21, 2025

Summary

This summary is machine-generated.

We developed a novel deep learning method for accurate automatic segmentation of the claustrum, a challenging brain structure. This approach works across various MRI contrasts and resolutions, improving neuroimaging research.

Keywords:

CNN claustrum contrast and resolution invariance ex vivo MRI segmentation synthetic images

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Last Updated: Sep 10, 2025

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

The claustrum, a gray matter structure, is difficult to visualize and segment in standard MRI due to its thin, sheet-like form.
Current neuroimaging tools and automatic segmentation methods for the claustrum are limited, hindering research into its functions.

Purpose of the Study:

To propose and validate a novel, contrast- and resolution-agnostic deep learning method for automatic segmentation of the claustrum.
To enable accurate segmentation of the claustrum at ultra-high resolution (0.35 mm isotropic) and standard resolutions (approx. 1 mm isotropic).

Main Methods:

Utilized the SynthSeg framework, which synthesizes training data with random contrast and resolution to achieve robust generalization.
Trained a deep learning network using manual claustrum labels from 18 ultra-high resolution MRI scans (primarily ex vivo).
Validated the method using 6-fold cross-validation on high-resolution scans and tested on in vivo T1-weighted MRI scans.

Main Results:

Achieved a Dice score of 0.632, mean surface distance of 0.458 mm, and volumetric similarity of 0.867 on ultra-high resolution MRI scans.
Demonstrated robustness on in vivo T1-weighted scans at typical resolutions and across multimodal imaging (T2-weighted, proton density, quantitative T1).
Confirmed method's reliability in test-retest scenarios.

Conclusions:

This is the first accurate, automatic method for ultra-high resolution claustrum segmentation that is robust to variations in contrast and resolution.
The developed method significantly advances the study of the claustrum by providing a reliable segmentation tool.
The method is publicly available as part of the SynthSeg framework and FreeSurfer, facilitating broader research applications.