ArticleArtificial intelligence in medicine2023
Automatic semantic segmentation of the lumbar spine: Clinical applicability in a multi-parametric and multi-center study on magnetic resonance images.
Article in Artificial intelligence in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2024Pooled it
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Advances in mechanical assessments of in vivo human lumbar spine tissues with noninvasive imaging techniques.npj biomedical innovations · 2026Review
- Radiologist-Validated Automatic Lumbar T1-Weighted Spinal MRI Segmentation Tool via an Attention U-Net Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.Scientific reports · 2025Article
- AI-Augmented Quantitative MRI Predicts Spontaneous Intracranial Hypotension.Diagnostics (Basel, Switzerland) · 2025Article
- The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Article
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.European radiology · 2025Article
- Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc.Scientific reports · 2025Article
- Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level.JOR spine · 2024Article
- Post-secondary classroom teaching quality evaluation using small object detection model.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significant difficulties in medical image segmentation include the high variability of images caused by their origin (multi-center), the acquisition protocols (multi-parametric), the variability of human anatomy, illness severity, the effect of age and gender, and notable other factors. This work addresses problems associated with the automatic semantic segmentation of lumbar spine magnetic resonance images using convolutional neural networks. We aimed to assign a class label to each pixel of an image, with classes defined by radiologists corresponding to structural elements such as vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies represent variants of the U-Net architecture, and we used several complementary blocks to define the variants: three types of convolutional blocks, spatial attention models, deep supervision, and multilevel feature extractor. Here, we describe the topologies and analyze the results of the neural network designs that obtained the most accurate segmentation. Several proposed designs outperform the standard U-Net used as a baseline, primarily when used in ensembles, where the outputs of multiple neural networks are combined according to different strategies.
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.