ArticleScientific reports2021
2-step deep learning model for landmarks localization in spine radiographs.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.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 · 2022Pooled it
- Large Language Model and Pediatric Neurosurgery - A Neurosurgeon's Perspective on the Artificial Nervous System.Journal of Korean Neurosurgical Society · 2026Article
- Automatic detection of spinal pathologies based on MRI scans.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
- Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.NPJ digital medicine · 2026Article
- Quantifying central canal stenosis prediction uncertainty in SpineNet with conformal prediction.Scientific reports · 2026Article
- Controlled comparative study of YOLOv8-Pose, YOLOv11-Pose, and Detectron2 for vertebrae detection and keypoint estimation.PloS one · 2026Article
- Simulating Federated Learning to Enable Multi-Hospital Collaboration for Lumbopelvic Alignment Estimation.JOR spine · 2025Article
- Clinical validation of a deep learning tool for characterizing spinopelvic mobility in total hip arthroplasty.Scientific reports · 2025Article
- Lumbar region perception segmentation: a novel algorithm for automated vertebral motion parameter measurement.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances.Diagnostics (Basel, Switzerland) · 2025Article
- Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models.La Radiologia medica · 2025Article
- Automated radiographic metrics for diagnosing lumbar spine instability: a cross-sectional observational study.Quantitative imaging in medicine and surgery · 2025Article
- Anatomical landmark detection on bi-planar radiographs for predicting spinopelvic parameters.Spine deformity · 2025Article
- Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset.Global spine journal · 2025Article
- Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.La Radiologia medica · 2025Article
- Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.Journal of imaging informatics in medicine · 2025Article
- Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature.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 · 2024Article
- Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.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 · 2024Article
- Clinical benchmark dataset for AI accuracy analysis: quantifying radiographic annotation of pelvic tilt.Scientific data · 2024Article
- Automatic measurement of anatomical parameters of the lumbar vertebral body and the intervertebral disc on radiographs by deep learning.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work we propose to use Deep Learning to automatically calculate the coordinates of the vertebral corners in sagittal x-rays images of the thoracolumbar spine and, from those landmarks, to calculate relevant radiological parameters such as L1-L5 and L1-S1 lordosis and sacral slope. For this purpose, we used 10,193 images annotated with the landmarks coordinates as the ground truth. We realized a model that consists of 2 steps. In step 1, we trained 2 Convolutional Neural Networks to identify each vertebra in the image and calculate the landmarks coordinates respectively. In step 2, we refined the localization using cropped images of a single vertebra as input to another convolutional neural network and we used geometrical transformations to map the corners to the original image. For the localization tasks, we used a differentiable spatial to numerical transform (DSNT) as the top layer. We evaluated the model both qualitatively and quantitatively on a set of 195 test images. The median localization errors relative to the vertebrae dimensions were 1.98% and 1.68% for x and y coordinates respectively. All the predicted angles were highly correlated with the ground truth, despite non-negligible absolute median errors of 1.84°, 2.43° and 1.98° for L1-L5, L1-S1 and SS respectively. Our model is able to calculate with good accuracy the coordinates of the vertebral corners and has a large potential for improving the reliability and repeatability of measurements in clinical tasks.
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