ReviewMagma (New York, N.Y.)2016
Segmentation of joint and musculoskeletal tissue in the study of arthritis.
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 34 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
34 citing papers in PubMed, 85 citations in OpenAlex.
- nnUnet-based automated quantification of wrist joint synovitis volume in patients with rheumatoid arthritis: a feasibility study.Radiology advances · 2026Article
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- Knee focal cartilage defect location heat map and local surface morphology characterisation: Insights for focal knee resurfacing implant design.Journal of experimental orthopaedics · 2025Article
- Arthritis Foundation/HSS Workshop on Hip Osteoarthritis, Part 2: Detecting Hips at Risk: Early Biomechanical and Structural Mechanisms.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2023Review
- Segmentation quality assessment by automated detection of erroneous surface regions in medical images.Computers in biology and medicine · 2023Article
- Patch-wise 3D segmentation quality assessment combining reconstruction and regression networks.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Diffusion-weighted MRI of total hip arthroplasty for classification of synovial reactions: A pilot study.Magnetic resonance imaging · 2023Article
- Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.The Journal of rheumatology · 2022Review
- Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Entropy and distance maps-guided segmentation of articular cartilage: data from the Osteoarthritis Initiative.International journal of computer assisted radiology and surgery · 2022Article
- Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative.Magma (New York, N.Y.) · 2021Article
- A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative.Journal of digital imaging · 2021Article
- Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.Magma (New York, N.Y.) · 2021Article
- Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma.Journal of orthopaedic surgery and research · 2021Article
- Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study.Journal of clinical medicine · 2021Article
- The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2020Article
- Automated multi-atlas segmentation of gluteus maximus from Dixon and T1-weighted magnetic resonance images.Magma (New York, N.Y.) · 2020Article
- Image-based biomechanical models of the musculoskeletal system.European radiology experimental · 2020Review
- Improving Quantitative Magnetic Resonance Imaging Using Deep Learning.Seminars in musculoskeletal radiology · 2020Review
- An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.Journal of digital imaging · 2020Article
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 1 institution in 1 country.
Funding
Abstract
As the most frequent cause of physical disability, musculoskeletal diseases such as arthritis and osteoporosis have a great social and economical impact. Quantitative magnetic resonance imaging (MRI) biomarkers are important tools that allow clinicians to better characterize, monitor, and even predict musculoskeletal disease progression. Post-processing pipelines often include image segmentation. Manually identifying the border of the region of interest (ROI) is a difficult and time-consuming task. Manual segmentation is also affected by inter- and intrauser variability, thus limiting standardization. Fully automatic or semi-automatic methods that minimize the user interaction are highly desirable. Unfortunately, an ultimate, highly reliable and extensively evaluated solution for joint and musculoskeletal tissue segmentation has not yet been proposed, and many clinical studies still adopt fully manual procedures. Moreover, the clinical translation of several promising quantitative MRI techniques is highly affected by the lack of an established, fast, and accurate segmentation method. The goal of this review is to present some of the techniques proposed in recent literature that have been adopted in clinical studies for joint and musculoskeletal tissue analyses in arthritis patients. The most widely used MRI sequences and image processing algorithms employed to accomplish segmentation challenges will be discussed in this paper.
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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.