A reproducible evaluation of ANTs similarity metric performance in brain image registration
Open accessAbstract
The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.
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Local NCC performs competitively but has high variance, demonstrating the instability of the loss.
“Local NCC [6] performs competitively but has high variance, demonstrating the instability of the loss.”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [6]
Results §4.2 reports competitive brain-extraction performance for registrations using CC and a relatively large standard deviation for the (CC, CC) pairing (0.937 ± 0.0210). But the (CC, MI) pairing has a much smaller standard deviation (0.958 ± 0.0054), and the paper attributes improved performance to MI affine initialization. It does not conclude that the local NCC loss itself is unstable; page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDiffPose develops a sparse multiscale variant of local normalized cross correlation that is robust to local minima and faster to evaluate.
“we develop a sparse multiscale variant of local normalized cross correlation [6, 7] that is robust to local minima and faster to evaluate.”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [6]
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- Authors
- Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C.
- Affiliations
- University of Pennsylvania (US)
- New York State Psychiatric Institute (US)
- Corresponding author
- Avants, Brian B.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 54
- Issue
- 3
- Pages
- 2033-2044
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3065962
- OpenAlex
- W2136145485
- Cited by
- 4,796
- References
- 6
- Citations by year
- 2026: 483 · 2025: 563 · 2024: 545 · 2023: 473 · 2022: 489 · 2021: 459 · 2020: 439 · 2019: 362 · 2018: 286 · 2017: 225 · 2016: 155 · 2015: 119 · 2014: 97 · 2013: 61 · 2012: 24
- FWCI
- 36.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB006266-01)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- ants, brain image registration, similarity measures, mutual information, cross correlation function, diffeomorphic registration, affine registration, template fabrication, brain extraction, T1-weighted imaging, reproducibility, benchmarking
- MeSH terms
- Algorithms, Brain, Diagnostic Imaging, Head, Humans, Image Processing, Computer-Assisted, Models, Anatomic, Models, Neurological, Population, Software, Reproducibility of Results, Linear Models, Databases, Factual
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