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Related Experiment Video

Updated: Dec 21, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Reliability assessment of tissue classification algorithms for multi-center and multi-scanner data.

Mahsa Dadar¹, Simon Duchesne¹, ¹

¹Department of Radiology and Nuclear Medicine, Faculty of Medicine, Laval University, Canada.

|May 16, 2020

Summary

This study evaluated six tissue classification pipelines for brain MRI scans. BISON demonstrated the most reliable gray matter segmentation and lowest volume variability across different scanners and sites.

Keywords:

Multi-center Multi-scanner Reliability tissue classification

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Area of Science:

Neuroimaging
Medical Image Analysis
Neurology & Psychiatry

Background:

Gray and white matter volume changes are key indicators in neurological and psychiatric diseases.
Tissue segmentation maps from image processing pipelines are crucial for these measures.
Reliability of segmentation across multi-center, multi-scanner MRI data is not well understood.

Purpose of the Study:

To assess the robustness of six publicly available tissue classification pipelines.
To evaluate pipeline performance using multi-center and multi-scanner T1-weighted MRI data.

Main Methods:

Utilized 90 T1-weighted MRI scans from a single individual across 27 sites and 73 sessions.
Assessed variability in Dice similarity index and tissue volumes for Atropos, BISON, Classify_Clean, FAST, FreeSurfer, and SPM12.

Main Results:

BISON showed the highest Dice coefficient for gray matter (GM) and lowest volume variability.
Atropos led in white matter (WM) Dice coefficient, followed by BISON and SPM12.
Significant differences in GM/WM estimates were observed across scanner manufacturers and signal-to-noise ratio (SNR) levels.

Conclusions:

Provides a benchmark for the reliability of common tissue classification techniques in large neuroimaging databases.
Highlights expected variability when using multi-center and multi-scanner MRI data.
Informs selection of appropriate pipelines for robust neuroimaging analysis.