The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
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 (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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Cortical Surface-Based Analysis
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Self-citationFischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Self-citationSequence-independent segmentation of magnetic resonance images
Fischl, B. R., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,117
Self-citationAdaptive segmentation of MRI data
Wells, W. M., Grimson, W. E. L., Kikinis, R., & Jolesz, F. A. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.511747 · Cited by 1,291
An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex
Essen, D. C. V., Drury, H. A., Dickson, J., Harwell, J., Hanlon, D., & Anderson, C. H. (2001) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2001.0080443 · Cited by 1,100
New variants of a method of MRI scale standardization
Nyúl, L. G., Udupa, J. K., & Zhang, X. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.836373 · Cited by 958
BrainSuite: An automated cortical surface identification tool
Shattuck, D. W., & Leahy, R. M. (2002) · Medical Image Analysis · doi:10.1016/s1361-8415(02)00054-3 · Cited by 864
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On standardizing the MR image intensity scale
Ny�l, L. G., & Udupa, J. K. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199912)42:6 · Cited by 575
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Towards a diffusion image processing validation and accuracy prediction framework
Ferrarese, F. P., Daducci, A., Cuadra, M. B., Lemkaddem, A., Granziera, C., Thiran, J., & Menegaz, G. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116091
Automated Quantitative Analysis of Cardiac Medical
Angeles, L. (2015)
Afroz, A. S., & Saha, P. (2015) · International Journal of Computer Applications · doi:10.5120/ijca2015907010
Automated Extraction of Biomarkers for Alzheimer's Disease from Brain Magnetic Resonance Images
Wolz, R. (2011) · Spiral (Imperial College London) · doi:10.25560/6996
Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue
V., U., S., S., & V., U. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017912784
Kradda, A. O., Ghomari, A., Hmed, A. B., & Binczak, S. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22542
Perfil neuropsicológico da síndrome de Turner: análise a partir do imprinting parental
Neto, G. S. S. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.17.2020.tde-02122020-103009
Optimización de una energía mediante cortes de grafos. Segmentación de imágenes
Puente, M. D. C. T. (2014) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.33010
Metadata
- 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
- 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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