ArticleSensors (Basel, Switzerland)2022
Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 71 citations in OpenAlex.
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Localization-guided segmentation of Gliomas via 3D ReFusionNet.Frontiers in oncology · 2026Article
- Intra- and inter-observer reliability of spinal inflection point localization: a cross-sectional radiographic study.Einstein (Sao Paulo, Brazil) · 2026Article
- YOLOBT: a novel ERP bad trial detection network dynamically adjusting based on global signal quality.Frontiers in human neuroscience · 2026Article
- A fully automatic Cobb angle measurement framework of full-spine DR images based on deep learning.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 · 2025Article
- Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images.Scientific reports · 2025Article
- Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation.Scientific reports · 2025Article
- Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.Journal of imaging informatics in medicine · 2025Article
- Accurate Intervertebral Disc Segmentation Approach Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images.Journal of dental sciences · 2024Article
- Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm.Scientific reports · 2023Article
- Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.Scientific reports · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- An approach to the diagnosis of lumbar disc herniation using deep learning models.Frontiers in bioengineering and biotechnology · 2023Article
- End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images.Computational intelligence and neuroscience · 2023Article
- X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data.Journal of imaging · 2022Article
- Prevalence of Monosodium Urate (MSU) Deposits in Cadavers Detected by Dual-Energy Computed Tomography (DECT).Diagnostics (Basel, Switzerland) · 2022Article
- Fast identification and quantification of c-Fos protein using you-only-look-once-v5.Frontiers in psychiatry · 2022Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29° and 0.38°, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.
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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.