Multi-atlas segmentation of biomedical images: A survey

Iglesias, Juan Eugenio; Sabuncu, Mert R.

doi:10.1016/j.media.2015.06.012

Cited by 667Medical Image Segmentation TechniquesMedical Imaging and AnalysisBrain Tumor Detection and Classification

Multi-atlas segmentation of biomedical images: A survey

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Iglesias, Juan Eugenio, Sabuncu, Mert R.·2015·Medical Image Analysis

DOI 10.1016/j.media.2015.06.012PMC PMC4532640

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Abstract

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

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Consensus segmentation using multiple atlases may make segmentation more robust to sporadic registration errors/imperfections.
“In recent years, methods that use the multi atlas label fusion framework have obtained state-of-the-art accuracy, showing that consensus segmentation using multiple atlases may significantly improve the segmentation accuracy, while making it also more robust to sporadic registration errors/imperfections (Iglesias and Sabuncu, 2015; Warfield, 2017).”
From A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies (2020) · cites this paper as Iglesias and Sabuncu, 2015
The Historical Introduction describes combining labels from multiple registered atlases by majority voting, which supports the idea of consensus segmentation. But §2.3 (Registration; page unavailable) does not establish greater robustness to sporadic registration errors: it cautions that poor candidates from suboptimal registrations can worsen fusion. The claimed robustness is plausible, but is not stated in the cited passages. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Consensus segmentation using multiple atlases may significantly improve segmentation accuracy.
“In recent years, methods that use the multi atlas label fusion framework have obtained state-of-the-art accuracy, showing that consensus segmentation using multiple atlases may significantly improve the segmentation accuracy, while making it also more robust to sporadic registration errors/imperfections (Iglesias and Sabuncu, 2015; Warfield, 2017).”
From A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies (2020) · cites this paper as Iglesias and Sabuncu, 2015
Multi-atlas label fusion methods have obtained state-of-the-art accuracy in recent years.
“In recent years, methods that use the multi atlas label fusion framework have obtained state-of-the-art accuracy, showing that consensus segmentation using multiple atlases may significantly improve the segmentation accuracy, while making it also more robust to sporadic registration errors/imperfections (Iglesias and Sabuncu, 2015; Warfield, 2017).”
From A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies (2020) · cites this paper as Iglesias and Sabuncu, 2015
Using a library of atlases manually created by experts allows the automated segmentation approach to incorporate knowledge on the appearance and shape of the placenta in a diverse set of subjects and leads to highly robust segmentation results in new subjects.
“Using a library of atlases manually created by experts allows the automated segmentation approach to incorporate knowledge on the appearance and shape of the placenta in a diverse set of subjects and leads to highly robust segmentation results in new subjects.”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [15]
Multi-atlas segmentation has been applied successfully to many challenging segmentation problems such as identifying hippocampal subfields in MRI, the spinal cord, and the mitral valve in preoperative 3D echocardiography.
“This approach has been applied successfully to many challenging segmentation problems 15 such as identifying hippocampal subfields in magnetic resonance images (MRI) of the brain, 17,18 the spinal cord, 19 and the mitral valve in preoperative 3D echocardiography. 20”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [15]

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Automated bone segmentation from dental CBCT images using patch‐based sparse representation and convex optimization
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Atlas Encoding by Randomized Forests for Efficient Label Propagation
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Automatic hippocampal segmentation in temporal lobe epilepsy: Impact of developmental abnormalities
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Learning to Rank Atlases for Multiple-Atlas Segmentation
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Amygdalar Atrophy in Early Alzheimer’s Disease
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Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls
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Segmentation of the Pectoral Muscle in Breast MRI Using Atlas-Based Approaches
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Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation
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Rapid automatic segmentation of the human cerebellum and its lobules (RASCAL)—Implementation and application of the patch‐based label‐fusion technique with a template library to segment the human cerebellum
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Using Frankenstein’s Creature Paradigm to Build a Patient Specific Atlas
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Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA‐based analysis
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Hierarchical performance estimation in the statistical label fusion framework
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Multiatlas segmentation of thoracic and abdominal anatomy with level set‐based local search
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Crowdsourcing for Reference Correspondence Generation in Endoscopic Images
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Evaluation of multi-atlas-based segmentation of CT scans in prostate cancer radiotherapy
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A Multi-Atlas Based Method for Automated Anatomical Rat Brain MRI Segmentation and Extraction of PET Activity
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Robust Statistical Fusion of Image Labels
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Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation
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Multi-atlas Segmentation with Robust Label Transfer and Label Fusion
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Multi-atlas Segmentation without Registration: A Supervoxel-Based Approach
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Multimodal Brain Image Analysis
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Incorporating Priors on Expert Performance Parameters for Segmentation Validation and Label Fusion: A Maximum a Posteriori STAPLE
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Breast lesion co-localisation between X-ray and MR images using finite element modelling
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Robust optic nerve segmentation on clinically acquired computed tomography
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Segmenting Images by Combining Selected Atlases on Manifold
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Efficient Selection of the Most Similar Image in a Database for Critical Structures Segmentation
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Striatal Morphology is Associated with Tobacco Cigarette Craving
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Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry
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Authors: Iglesias, J. E., & Sabuncu, M. R.
Affiliations: Basque Center on Cognition, Brain and Language (ES)
Harvard University (US)
Corresponding author: Sabuncu, Mert R.
ORCID: Iglesias, Juan Eugenio
Sabuncu, Mert R.
Venue: Medical Image Analysis
Publisher: Elsevier BV
Published: 2015-08
Volume: 24
Issue: 1
Pages: 205-219
ISSN: 1361-8415 (ISSN-L)
DOI: 10.1016/j.media.2015.06.012
PMC: PMC4532640
OpenAlex: W1667869507
Cited by: 667
References: 267
Citations by year: 2026: 28 · 2025: 40 · 2024: 42 · 2023: 48 · 2022: 60 · 2021: 84 · 2020: 83 · 2019: 97 · 2018: 72 · 2017: 64 · 2016: 42 · 2015: 7
FWCI: 53.55
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Medical Image Analysis (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: NIH (1K25EB013649-01)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
Keywords: multi-atlas segmentation, medical image segmentation, anatomical variation, stochastic modeling, optimization, computer vision, machine learning, computational cost
MeSH terms: Algorithms, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results

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