ReviewMagma (New York, N.Y.)2016
Segmentation and quantification of adipose tissue by magnetic resonance imaging.
Review in Magma (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 91 citations in OpenAlex.
- Chronic Insufficient Sleep in Women Impairs Insulin Sensitivity Independent of Adiposity Changes: Results of a Randomized Trial.Diabetes care · 2024Trial
- Exploring Adipose Tissue Complexity Through Omics Approaches: Implications for Health and Disease.Cells · 2026Review
- Refining visceral adipose tissue quantification: Influence of sex, age, and BMI on single slice estimation in 3D MRI of the German National Cohort.Zeitschrift fur medizinische Physik · 2026Article
- Reference Methods for Measuring Skeletal Muscle Mass: A Critical Perspective.Journal of cachexia, sarcopenia and muscle · 2026Review
- Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation and quantification using free-breathing 3D MRI.Radiology advances · 2026Article
- Evolving perspectives on evaluating obesity: from traditional methods to cutting-edge techniques.Annals of medicine · 2025Review
- Automated detection of epicardial adipose tissue in cardiac CT using ensemble machine learning for improved diagnosis.MethodsX · 2025Article
- BioCompNet: A Deep Learning Workflow Enabling Automated Body Composition Analysis toward Precision Management of Cardiometabolic Disorders.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Fundamental Body Composition Principles Provide Context for Fat-Free and Skeletal Muscle Loss With GLP-1 RA Treatments.Journal of the Endocrine Society · 2024Review
- Continuum topological derivative - A novel application tool for segmentation of CT and MRI images.Neuroimage. Reports · 2024Article
- Quantification of Visceral Fat at the L5 Vertebral Body Level in Patients with Crohn's Disease Using T2-Weighted MRI.Bioengineering (Basel, Switzerland) · 2024Article
- Hepatocellular carcinoma in patients with non-alcoholic fatty liver disease is associated with heterogeneous pattern of fat infiltration in skeletal muscles.European radiology · 2024Article
- Fat Matters: Exploring Cancer Risk through the Lens of Computed Tomography and Visceral Adiposity.Journal of clinical medicine · 2024Review
- Body Composition Evaluation and Clinical Markers of Cardiometabolic Risk in Patients with Phenylketonuria.Nutrients · 2023Review
- Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.Tomography (Ann Arbor, Mich.) · 2023Article
- A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging.Tomography (Ann Arbor, Mich.) · 2023Article
- Article
- Structural changes in subcutaneous and visceral abdominal fatty tissue induced by local application of 448 kHz capacitive resistive monopolar radiofrequency: a magnetic resonance imaging case study.Lasers in medical science · 2022Article
- Adipose tissue measurement in clinical research for obesity, type 2 diabetes and NAFLD/NASH.Endocrinology, diabetes & metabolism · 2022Review
- Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning.Magma (New York, N.Y.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water-fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations.
Indexed as
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What OpenQuestion holds
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.