SynthesisAcademic radiology2019
Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.
Synthesis in Academic radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108310 (ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors), which is not on this map. Cited by 69 papers, 3 of them syntheses that pooled it.
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.
ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors
Who cites it
69 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.BMC medical informatics and decision making · 2026Pooled it
- High variability exists in 3D leg alignment analysis, but underlying principles that might lead to agreement on a universal framework could be identified: A systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Pooled it
- Expert Consensus on Morphofunctional Assessment in Disease-Related Malnutrition. Grade Review and Delphi Study.Nutrients · 2023Pooled it
- Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021Trial
- Reference-Label Construction and Annotation Reporting in CT- and MRI-Based Adipose Tissue Segmentation: A Systematic Review and Evidence Map.Journal of imaging informatics in medicine · 2026Review
- Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning.European radiology · 2026Article
- Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models.World journal of radiology · 2026Article
- Automated 3D chest CT muscle segmentation-derived model development and explanation in discrimination of normal, preserved ratio impaired spirometry, and COPD: a multicenter study.Insights into imaging · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- Blinded, bias-controlled multi-rater evaluation of human-versus-AI brain metastasis segmentation using a hybrid foundation-model framework.Medical physics · 2026Article
- Intra- and inter-rater reliability of skeletal muscle mass MRI assessment among adults: a systematic review.Quantitative imaging in medicine and surgery · 2026Review
- Automated vs. manual segmentation for small renal mass imaging.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Article
- Deep learning-based framework for comprehensive quantification of thigh and calf muscles and adipose tissues from MRI.Magma (New York, N.Y.) · 2026Article
- Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Body Composition Analysis Using DAFS Express on 2D MRI Slices at L3 Vertebral Level.Journal of imaging informatics in medicine · 2026Article
- Head circumference assessment in pediatric MRI: a pilot study of manual measurement methods and automated segmentation-based alternatives.Frontiers in neuroscience · 2026Article
- Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.Radiology advances · 2026Article
- AI-Based 3D Liver Segmentation and Volumetric Analysis in Living Donor Data.Journal of imaging informatics in medicine · 2025Article
- Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys.Computers in biology and medicine · 2025Article
- Associations of CT Muscle Area and Density With Functional Outcomes and Mortality Across Anatomical Regions in Older Men.Journal of the American Geriatrics Society · 2025Article
9 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
14 authors.
Funding
Abstract
RATIONALE AND
objectivesThe automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. MATERIALS AND
methodsThe Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic.
resultsThe systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications.
conclusionThese insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.
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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.