Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration
Abstract
Although many atlas-based segmentation methods have been developed and validated for the human brain, limited work has been done for the mouse brain. This paper investigated roles of image registration and segmentation model complexity in the mouse brain segmentation. We employed four segmentation models [single atlas, multiatlas, simultaneous truth and performance level estimation (STAPLE) and Markov random field (MRF) via four different image registration algorithms (affine, B-spline free-form deformation (FFD), Demons and large deformation diffeomorphic metric mapping (LDDMM)] for delineating 19 structures from in vivo magnetic resonance microscopy images. We validated their accuracies against manual segmentation. Our results revealed that LDDMM outperformed Demons, FFD and affine in any of the segmentation models. Under the same registration, increasing segmentation model complexity from single atlas to multiatlas, STAPLE or MRF significantly improved the segmentation accuracy. Interestingly, the multiatlas-based segmentation using nonlinear registrations (FFD, Demons and LDDMM) had similar performance to their STAPLE counterparts, while they both outperformed their MRF counterparts. Furthermore, when the single-atlas affine segmentation was used as reference, the improvement due to nonlinear registrations (FFD, Demons and LDDMM) in the single-atlas segmentation model was greater than that due to increasing model complexity (multiatlas, STAPLE and MRF affine segmentation)…
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In Bai et al. (2012), image registration plays a more crucial role in segmentation compared to the complexity of the segmentation model.
“the authors investigated the roles of image registration and segmentation model complexity for mouse brain segmentation using 4 different registration algorithms, and concluded that image registration plays a more crucial role in segmentation compared to the complexity of the segmentation model.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Bai et al. (2012)
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“In Bai et al. (2012), the authors investigated the roles of image registration and segmentation model complexity for mouse brain segmentation using 4 different registration algorithms, and concluded that image registration plays a more crucial role in segmentation compared to the complexity of the…”
Ma, D., Holmes, H. E., Cardoso, M. J., Modat, M., Harrison, I. F., Powell, N. M., O’Callaghan, J. M., Ismail, O., Johnson, R. A., O’Neill, M. J., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00011 · Cited by 36 · see citing works
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Cui, M., Suzuka, S. M., Branch, N. A., Ambadipudi, K., Thangaswamy, S., Acharya, S. A., Billett, H. H., & Branch, C. A. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3692 · Cited by 11 · see citing works
- Authors
- Bai, J., Trinh, T. L. H., Chuang, K.-H., & Qiu, A.
- Affiliations
- National University of Singapore (SG)
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Qiu, Anqi
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 30
- Issue
- 6
- Pages
- 789-798
- ISSN
- 0730-725X (ISSN-L)
- OpenAlex
- W2171811623
- Cited by
- 64
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 5 · 2021: 8 · 2020: 5 · 2019: 9 · 2018: 3 · 2017: 5 · 2016: 6 · 2015: 7 · 2014: 3 · 2013: 3 · 2012: 3
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- Funders
- National University of Singapore
- Ministry of Earth Sciences
- Ministry of Education, India
- National Medical Research Council
- 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
- atlas-based segmentation, image registration, staples, Markov random fields, LDDMM, demons registration, affine registration, brain structure delineation
- MeSH terms
- Algorithms, Animals, Brain, Magnetic Resonance Imaging, Male, Mice, Inbred C57BL, Mice
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