ArticleJournal of applied clinical medical physics2023
Lumbar spine segmentation method based on deep learning.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 24 citations in OpenAlex.
- Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.Scientific reports · 2025Article
- Text-guided multimodal deep learning in magnetic resonance imaging for spinal structures segmentation and lumbar abnormalities identification.Quantitative imaging in medicine and surgery · 2025Article
- Quantification of Thoracic Volume and Spinal Length of Pediatric Scoliosis Patients on Chest MRI Using a 3D U-Net Segmentation.Healthcare (Basel, Switzerland) · 2025Article
- Can artificial intelligence in spine imaging affect current practice? Practical developments and their clinical status.North American Spine Society journal · 2025Article
- Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.Tomography (Ann Arbor, Mich.) · 2024Article
- Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion.Bioengineering (Basel, Switzerland) · 2024Article
- Lumbar spine segmentation method based on deep learning.Journal of applied clinical medical physics · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aiming at the difficulties of lumbar vertebrae segmentation in computed tomography (CT) images, we propose an automatic lumbar vertebrae segmentation method based on deep learning. The method mainly includes two parts: lumbar vertebra positioning and lumbar vertebrae segmentation. First of all, we propose a lumbar spine localization network of Unet network, which can directly locate the lumbar spine part in the image. Then, we propose a three-dimensional XUnet lumbar vertebrae segmentation method to achieve automatic lumbar vertebrae segmentation. The method proposed in this paper was validated on the lumbar spine CT images on the public dataset VerSe 2020 and our hospital dataset. Through qualitative comparison and quantitative analysis, the experimental results show that the method proposed in this paper can obtain good lumbar vertebrae segmentation performance, which can be further applied to detection of spinal anomalies and surgical treatment.
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