ReviewMedical image analysis2015
Multi-atlas segmentation of biomedical images: A survey.
Review in Medical image analysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 248 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
248 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis.Dento maxillo facial radiology · 2020Pooled it
- Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model.PloS one · 2021Trial
- A Multi-atlas Approach for Active Bone Marrow Sparing Radiation Therapy: Implementation in the NRG-GY006 Trial.International journal of radiation oncology, biology, physics · 2020Trial
- Clinical evaluation of three deep learning contouring models for head and neck organs at risk and lymph node levels.Technical innovations & patient support in radiation oncology · 2026Article
- Practical deep learning solutions for prostate cancer segmentation and implementation in radiotherapy planning.Physical and engineering sciences in medicine · 2026Article
- Multi-Atlas-Based Segmentation of Pediatric Vocal Tract Anatomy in Dynamic Magnetic Resonance Imaging.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Adaptive deep learning for quantification and spatial distribution of abdominal adipose tissue from magnetic resonance imaging proton density fat fraction in adults with a body mass index ≥24 kg/mQuantitative imaging in medicine and surgery · 2026Article
- Real-world clinical impact of implementing and updating a deep learning-based automatic contouring system in rectal cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- MARBLE: An in vivo MRI marker of limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026Review
- Cross-Subject Registration-Based Augmentation: Alleviating Anatomical Misalignment in Trauma CT for Robust Hemorrhage Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Subthalamic segmentations in relation to deep brain stimulation volumes in Parkinson's disease.Acta neurochirurgica · 2026Article
- A comparative evaluation of commercial software programs for automatic segmentation in breast cancer radiotherapy planning.Radiological physics and technology · 2026Article
- Inter-observer variability in radiotherapy contouring with the use of autocontouring software: A systematic review.Clinical and translational radiation oncology · 2026Review
- On the accuracy of image registration in portable low-field 3D brain MRI.Research square · 2026Article
- Redefining Idiopathic Normal Pressure Hydrocephalus Using AI-Driven Brain Volumetry.Biomedicines · 2026Article
- Atlas selection methods for multi-atlas-based segmentation in breast cancer radiotherapy planning.Radiological physics and technology · 2026Article
- On the accuracy of image registration in portable low-field 3D brain MRI.bioRxiv : the preprint server for biology · 2026Article
- Precision of automatically generated generic regions of interest for direct assessment of perfusion-MRI.Frontiers in physiology · 2026Article
- InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation.Brain communications · 2026Article
188 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.