Healthc Inform Res.  2020 Jan;26(1):61-67. 10.4258/hir.2020.26.1.61.

Web-Based Spine Segmentation Using Deep Learning in Computed Tomography Images

• Kim YJ
• Ganbold B
• Kim KG

• KMID: 2469401
• DOI: http://doi.org/10.4258/hir.2020.26.1.61

Abstract

OBJECTIVES
Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is a very common problem among adolescents, and the highest incidence rates are for adults in their 30s. There has been a remarkable increase in using computer-aided diagnosis to assist doctors in the interpretation of medical images. Spine segmentation in computed tomography (CT) scans using algorithmic methods allows improved diagnosis of back pain.
METHODS
In this study, we developed a web-based automatic spine segmentation method using deep learning and obtained the dice coefficient by comparison with the predicted image. Our method is based on convolutional neural networks for segmentation. More specifically, we train a hierarchical data format file using U-Net architecture and then insert the test data label to perform segmentation. Thus, we obtained more specific and detailed results. A total of 344 CT images were used in the experiment. Of these, 330 were used for learning, and the remaining 14 for testing.
RESULTS
Our method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%.
CONCLUSIONS
The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.

Keyword

Deep Learning; Computer-Aided Diagnosis; Health Information Systems; Classification; Spine

MeSH Terms

Adolescent
Adult
Back Pain
Classification
Diagnosis
Health Information Systems
Humans
Incidence
Learning*
Low Back Pain
Methods
Spine*

Figure

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  Figure 1 Process of spine segmentation using the U-Net architecture. CT: computed tomography.

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  Figure 2 Web-based spine segmentation process.

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  Figure 3 Examples of spinal area segmentation results based on deep learning: (A, C, E) computed tomography images and (B, D, F) deep learning-based spinal segmentation results.

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  Figure 4 Bland-Altman plot between deep learning-based segmentation and manual segmentation results.

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  Figure 5 User interface of webpage for uploading files.

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  Figure 6 User interface of webpage to provide spinal segmentation results using deep learning.

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