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High-resolution dataset of manual claustrum segmentation.

Adam Coates^1,2, Natalia Zaretskaya^1,2

¹Department of Psychology, University of Graz, Graz, Austria.

|July 4, 2024

Summary

This study presents the first high-resolution manual segmentation of the entire claustrum, including the challenging ventral "puddles." These detailed labels improve anatomical MRI analysis for researchers studying the human brain.

Keywords:

Claustrum High-resolution Label MNI space MRI Manual Region of interest Segmentation

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Area of Science:

Neuroimaging
Neuroanatomy
Brain Mapping

Background:

The claustrum's thin, sheet-like structure complicates identification in standard anatomical MRI.
Existing automated and atlas-based segmentation methods often exclude the ventral claustrum, known as "puddles."

Purpose of the Study:

To create a comprehensive, high-resolution manual segmentation of the entire human claustrum.
To provide a reliable dataset for improving claustrum localization in in vivo MRI scans.

Main Methods:

Manual segmentation of the whole claustrum on ultra-high resolution postmortem MRI data.
Independent labeling by four trainees, with union and Dice coefficient analysis for label correspondence.
Size measurements in MNI space using oriented bounding box calculations.

Main Results:

The first manual segmentation labels encompassing both dorsal and ventral claustrum regions at high resolution.
Validated label correspondence using Dice coefficients between independent raters.
Provided size metrics for the segmented claustrum in standard MNI space.

Conclusions:

This dataset offers a significant advancement for claustrum research by including all its parts.
The high-resolution manual labels can aid in approximating claustrum location in typical in vivo MRI scans.