ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2022
Efficient cascaded V-net optimization for lower extremity CT segmentation validated using bone morphology assessment.
Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Segmentation methods for quantifying X-ray Computed Tomography based biomarkers to assess hip fracture risk: a systematic literature review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- Progression of bone and joint space deformity in patients with mild knee osteoarthritis: Data from the IMI-APPROACH cohort.Osteoarthritis and cartilage open · 2026Article
- Combining deep learning with statistical shape modelling enables automated lower limb measurements with observer-level reliability using weight-bearing computed tomography.Journal of experimental orthopaedics · 2026Article
- Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset.Frontiers in medicine · 2025Article
- Fully automated workflow for designing patient-specific orthopaedic implants: Application to total knee arthroplasty.PloS one · 2025Article
- Risk stratification by nomogram of deep learning radiomics based on multiparametric magnetic resonance imaging in knee meniscus injury.International orthopaedics · 2023Article
- A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images.European radiology experimental · 2023Article
- Efficient cascaded V-net optimization for lower extremity CT segmentation validated using bone morphology assessment.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 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
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Semantic segmentation of bone from lower extremity computerized tomography (CT) scans can improve and accelerate the visualization, diagnosis, and surgical planning in orthopaedics. However, the large field of view of these scans makes automatic segmentation using deep learning based methods challenging, slow and graphical processing unit (GPU) memory intensive. We investigated methods to more efficiently represent anatomical context for accurate and fast segmentation and compared these with state-of-the-art methodology. Six lower extremity bones from patients of two different datasets were manually segmented from CT scans, and used to train and optimize a cascaded deep learning approach. We varied the number of resolution levels, receptive fields, patch sizes, and number of V-net blocks. The best performing network used a multi-stage, cascaded V-net approach with 128
Indexed as
Identifiers
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.