ArticleAJNR. American journal of neuroradiology2019
Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
- 3D U-net for volumetric segmentation of spinal cord MRI in degenerative disorders.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Artificial Intelligence for Procedural Guidance of Spinal Epidural Steroid Injections: A Scoping Review.Cureus · 2026Review
- Hotspots and Trends in the Application of Artificial Intelligence in Spine Medicine from 2005 to 2024: A Bibliometric and Visualization Analysis.Indian journal of orthopaedics · 2026Article
- Quantifying the Trajectory of Percutaneous Endoscopic Lumbar Discectomy in 3D Lumbar Models Based on Automated MR Image Segmentation-A Cross-Sectional Study.Orthopaedic surgery · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.Journal of applied clinical medical physics · 2024Article
- Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.Diagnostics (Basel, Switzerland) · 2023Article
- Interrelations Between the Too-Long Anterior Calcaneal Process, Hind and Mid-tarsal Bone Volumes, Angles and Osteochondral Lesion of the Dome of the Talus: Analysis by Software Slicer of 69 CT Scan of Feet.Indian journal of orthopaedics · 2022Article
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Current development and prospects of deep learning in spine image analysis: a literature review.Quantitative imaging in medicine and surgery · 2022Review
- Machine Learning Algorithms in Neuroimaging: An Overview.Acta neurochirurgica. Supplement · 2022Review
- A novel dual-network architecture for mixed-supervised medical image segmentation.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021Article
- Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.Frontiers in endocrinology · 2020Article
- Mixed-Supervised Dual-Network for Medical Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
- Lumbar safety triangle: comparative study of coronal and coronal oblique planes in 3.0-T magnetic resonance imaging.Radiologia brasileiraArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
background and purpose3D reconstruction of a targeted area ("safe" triangle and Kambin triangle) may benefit the viability assessment of transforaminal epidural steroid injection, especially at the L5/S1 level. However, manual segmentation of lumbosacral nerves for 3D reconstruction is time-consuming. The aim of this study was to investigate the feasibility of deep learning-based segmentation of lumbosacral nerves on CT and the reconstruction of the safe triangle and Kambin triangle. MATERIALS AND
methodsA total of 50 cases of spinal CT were manually labeled for lumbosacral nerves and bones using Slicer 4.8. The ratio of training/validation/testing was 32:8:10. A 3D U-Net was adopted to build the model SPINECT for automatic segmentations of lumbosacral structures. The Dice score, pixel accuracy, and Intersection over Union were computed to assess the segmentation performance of SPINECT. The areas of Kambin and safe triangles were measured to validate the 3D reconstruction.
resultsThe results revealed successful segmentation of lumbosacral bone and nerve on CT. The average pixel accuracy for bone was 0.940, and for nerve, 0.918. The average Intersection over Union for bone was 0.897 and for nerve, 0.827. The Dice score for bone was 0.945, and for nerve, it was 0.905. There were no significant differences in the quantified Kambin triangle or safe triangle between manually segmented images and automatically segmented images (
conclusionsDeep learning-based automatic segmentation of lumbosacral structures (nerves and bone) on routine CT is feasible, and SPINECT-based 3D reconstruction of safe and Kambin triangles is also validated.
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.