Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms
Abstract
Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.
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Partial volume error correction was performed for florbetapir SUVRs with the geometric transfer matrix method using Freesurfer segmented regions, and all florbetapir results shown in tables and figures are from PVE corrected data.
“Partial volume error (PVE) correction was performed for florbetapir SUVRs with the geometric transfer matrix (GTM) method51 using Freesurfer segmented regions52 (all florbetapir results shown in the tables and figures are from PVE corrected data).”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [52]
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- Authors
- Han, X., & Fischl, B.
- Affiliations
- Harvard University (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2007-04
- Volume
- 26
- Issue
- 4
- Pages
- 479-486
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2111934016
- Cited by
- 215
- References
- 26
- Citations by year
- 2025: 4 · 2024: 7 · 2023: 4 · 2022: 7 · 2021: 3 · 2020: 9 · 2019: 15 · 2018: 14 · 2017: 15 · 2016: 14 · 2015: 19 · 2014: 22 · 2013: 16 · 2012: 9
- FWCI
- 19.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB001550, U54EB005149)
- National Center for Research Resources (P41-RR-14075, U24RR021382, R01 RR1659401A1, P41RR014075, R01RR016594)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- brain MRI segmentation, atlas-based segmentation, whole-brain segmentation, hippocampus segmentation
- MeSH terms
- Algorithms, Anatomy, Artistic, Artificial Intelligence, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Medical Illustration, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Imaging, Three-Dimensional
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