Automated claustrum segmentation in human brain MRI using deep learning

Hongwei Li, Aurore Menegaux, Benita Schmitz‐Koep, Antonia Neubauer, Felix J.B. Bäuerlein, Suprosanna Shit, Christian Sorg, Bjoern H. Menze et al.

Human Brain Mapping 2021 article cited by 28

Source Open-access PDF More in Medicine & Health

importance 4/10 partially solved Medical Image Segmentation Techniques neuroimaging stage: research

Problem

The claustrum's delicate, sheet-like structure made it infeasible to segment with conventional methods in human brain MRI, limiting in vivo research.

Why it matters

The claustrum is implicated in forebrain function, but few human in vivo studies exist without reliable segmentation.

What the AI did

A multi-view deep learning approach segmented the claustrum in T1-weighted MRI, trained and evaluated in 181 individuals against expert neuroradiologist annotations.

Results

Cross-validation: median volumetric similarity 93.3%, robust Hausdorff distance 1.41 mm, Dice score 71.8%.

Impact

Provides automated claustrum segmentation validated against expert manual annotations in 181 individuals.

Record

Venue	Human Brain Mapping
Published	2021
Work type	article
Authors	Hongwei Li, Aurore Menegaux, Benita Schmitz‐Koep, Antonia Neubauer, Felix J.B. Bäuerlein, Suprosanna Shit, Christian Sorg, Bjoern H. Menze et al.
Citations	28 (OpenAlex, at import time)
References	49
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1002/hbm.25655
Open access	gold
Keywords	deep learning, claustrum, MRI segmentation, Dice score, neuroradiology

Importance rating

4 / 10

real but narrower advance

Enables in vivo human claustrum research previously infeasible; a real but narrow methodological advance for a specific brain structure.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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