Lumbar spine segmentation method based on deep learning

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 p...

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Bibliographic Details
Published in:Journal of applied clinical medical physics 2023-06, Vol.24 (6), p.e13996-n/a
Main Authors: Lu, Hongjiang, Li, Mingying, Yu, Kun, Zhang, Yingjiao, Yu, Liang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Classification
CT images
Datasets
Deep Learning
Hospitals
Humans
Image Processing, Computer-Assisted - methods
Localization
Lumbar Vertebrae - diagnostic imaging
lumbar vertebrae segmentation
Medical Imaging
Methods
Morphology
Neural networks
Semantics
spinal anomalies
Spine
surgical treatment
Tomography
Tomography, X-Ray Computed - methods
Vertebrae
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Summary: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.
ISSN:1526-9914
1526-9914
DOI:10.1002/acm2.13996