Cited by 215Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationMedical Imaging and Analysis

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Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms

Han, Xiao, Fischl, Bruce·2007·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2007.893282

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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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Self-citation
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Authors: Han, X., & Fischl, B.
Affiliations: Harvard University (US)
ORCID: Han, Xiao
Fischl, Bruce
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2007-04
Volume: 26
Issue: 4
Pages: 479-486
ISSN: 0278-0062 (ISSN-L)
DOI: 10.1109/tmi.2007.893282
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › 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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Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms

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