A Deep-Learning–Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images Article Swipe
Haotian Shen
,
Jiawei Huang
,
Qiangqiang Zheng
,
Zhiwei Zhu
,
Xiaoqiang Lv
,
Yong Liu
,
Yue Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/ptj/pzab041
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/ptj/pzab041
Objective The paraspinal muscles have been extensively studied on axial lumbar magnetic resonance imaging (MRI) for better understanding of back pain; however, the acquisition of measurements mainly relies on manual segmentation, which is time consuming. The study objective was to develop and validate a deep-learning–based program for automated acquisition of quantitative measurements for major lumbar spine components on axial lumbar MRIs, the paraspinal muscles in particular. Methods This study used a cross-sectional observational design. From the Hangzhou Lumbar Spine Study, T2-weighted axial MRIs at the L4–5 disk level of 120 participants (aged 54.8 years [SD = 15.0]) were selected to develop the deep-learning–based program Spine Explorer (Tulong). Another 30 axial lumbar MRIs were automatically measured by Spine Explorer and then manually measured using ImageJ to acquire quantitative size and compositional measurements for bilateral multifidus, erector spinae, and psoas muscles; the disk; and the spinal canal. Intersection-over-union and Dice score were used to evaluate the performance of automated segmentation. Intraclass coefficients and Bland–Altman plots were used to examine intersoftware agreements for various measurements. Results After training, Spine Explorer (Tulong) measures an axial lumbar MRI in 1 second. The intersections-over-union were 83.3% to 88.4% for the paraspinal muscles and 92.2% and 82.1% for the disk and spinal canal, respectively. For various size and compositional measurements of paraspinal muscles, Spine Explorer (Tulong) was in good agreement with ImageJ (intraclass coefficient = 0.85 to approximately 0.99). Conclusion Spine Explorer (Tulong) is automated, efficient, and reliable in acquiring quantitative measurements for the paraspinal muscles, the disk, and the canal, and various size and compositional measurements were simultaneously obtained for the lumbar paraspinal muscles. Impact Such an automated program might encourage further epidemiological studies of the lumbar paraspinal muscle degeneration and enhance paraspinal muscle assessment in clinical practice.
Related Topics
Concepts
Magnetic resonance imaging
Lumbar
Intraclass correlation
Lumbar spine
Medicine
Segmentation
Back pain
Spinal canal
Sørensen–Dice coefficient
Low back pain
Anatomy
Computer science
Radiology
Artificial intelligence
Image segmentation
Spinal cord
Surgery
Clinical psychology
Psychiatry
Pathology
Psychometrics
Alternative medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/ptj/pzab041
- https://academic.oup.com/ptj/article-pdf/101/6/pzab041/41830383/pzab041.pdf
- OA Status
- bronze
- Cited By
- 31
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3128188367
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3128188367Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/ptj/pzab041Digital Object Identifier
- Title
-
A Deep-Learning–Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-31Full publication date if available
- Authors
-
Haotian Shen, Jiawei Huang, Qiangqiang Zheng, Zhiwei Zhu, Xiaoqiang Lv, Yong Liu, Yue WangList of authors in order
- Landing page
-
https://doi.org/10.1093/ptj/pzab041Publisher landing page
- PDF URL
-
https://academic.oup.com/ptj/article-pdf/101/6/pzab041/41830383/pzab041.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/ptj/article-pdf/101/6/pzab041/41830383/pzab041.pdfDirect OA link when available
- Concepts
-
Magnetic resonance imaging, Lumbar, Intraclass correlation, Lumbar spine, Medicine, Segmentation, Back pain, Spinal canal, Sørensen–Dice coefficient, Low back pain, Anatomy, Computer science, Radiology, Artificial intelligence, Image segmentation, Spinal cord, Surgery, Clinical psychology, Psychiatry, Pathology, Psychometrics, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
31Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 7, 2023: 6, 2022: 7, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Lumbar | 78 |
| abstract_inverted_index.Study, | 80 |
| abstract_inverted_index.better | 17 |
| abstract_inverted_index.canal, | 206, 254 |
| abstract_inverted_index.canal. | 145 |
| abstract_inverted_index.lumbar | 11, 55, 60, 111, 182, 266, 281 |
| abstract_inverted_index.mainly | 27 |
| abstract_inverted_index.manual | 30 |
| abstract_inverted_index.muscle | 283, 288 |
| abstract_inverted_index.relies | 28 |
| abstract_inverted_index.spinal | 144, 205 |
| abstract_inverted_index.Another | 108 |
| abstract_inverted_index.Methods | 67 |
| abstract_inverted_index.Results | 173 |
| abstract_inverted_index.acquire | 126 |
| abstract_inverted_index.design. | 74 |
| abstract_inverted_index.develop | 41, 101 |
| abstract_inverted_index.enhance | 286 |
| abstract_inverted_index.erector | 135 |
| abstract_inverted_index.examine | 167 |
| abstract_inverted_index.further | 276 |
| abstract_inverted_index.imaging | 14 |
| abstract_inverted_index.muscles | 4, 64, 196 |
| abstract_inverted_index.program | 46, 104, 273 |
| abstract_inverted_index.second. | 186 |
| abstract_inverted_index.spinae, | 136 |
| abstract_inverted_index.studied | 8 |
| abstract_inverted_index.studies | 278 |
| abstract_inverted_index.various | 171, 209, 256 |
| abstract_inverted_index.(Tulong) | 178, 219, 236 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Explorer | 106, 118, 177, 218, 235 |
| abstract_inverted_index.Hangzhou | 77 |
| abstract_inverted_index.clinical | 291 |
| abstract_inverted_index.evaluate | 153 |
| abstract_inverted_index.however, | 22 |
| abstract_inverted_index.magnetic | 12 |
| abstract_inverted_index.manually | 121 |
| abstract_inverted_index.measured | 115, 122 |
| abstract_inverted_index.measures | 179 |
| abstract_inverted_index.muscles, | 216, 249 |
| abstract_inverted_index.muscles. | 268 |
| abstract_inverted_index.muscles; | 139 |
| abstract_inverted_index.obtained | 263 |
| abstract_inverted_index.reliable | 241 |
| abstract_inverted_index.selected | 99 |
| abstract_inverted_index.validate | 43 |
| abstract_inverted_index.(Tulong). | 107 |
| abstract_inverted_index.Objective | 1 |
| abstract_inverted_index.acquiring | 243 |
| abstract_inverted_index.agreement | 223 |
| abstract_inverted_index.automated | 48, 157, 272 |
| abstract_inverted_index.bilateral | 133 |
| abstract_inverted_index.encourage | 275 |
| abstract_inverted_index.objective | 38 |
| abstract_inverted_index.practice. | 292 |
| abstract_inverted_index.resonance | 13 |
| abstract_inverted_index.training, | 175 |
| abstract_inverted_index.Conclusion | 233 |
| abstract_inverted_index.Intraclass | 159 |
| abstract_inverted_index.agreements | 169 |
| abstract_inverted_index.assessment | 289 |
| abstract_inverted_index.automated, | 238 |
| abstract_inverted_index.components | 57 |
| abstract_inverted_index.consuming. | 35 |
| abstract_inverted_index.efficient, | 239 |
| abstract_inverted_index.paraspinal | 3, 63, 195, 215, 248, 267, 282, 287 |
| abstract_inverted_index.(intraclass | 226 |
| abstract_inverted_index.T2-weighted | 81 |
| abstract_inverted_index.acquisition | 24, 49 |
| abstract_inverted_index.coefficient | 227 |
| abstract_inverted_index.extensively | 7 |
| abstract_inverted_index.multifidus, | 134 |
| abstract_inverted_index.particular. | 66 |
| abstract_inverted_index.performance | 155 |
| abstract_inverted_index.coefficients | 160 |
| abstract_inverted_index.degeneration | 284 |
| abstract_inverted_index.measurements | 26, 52, 131, 213, 245, 260 |
| abstract_inverted_index.participants | 91 |
| abstract_inverted_index.quantitative | 51, 127, 244 |
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| abstract_inverted_index.automatically | 114 |
| abstract_inverted_index.compositional | 130, 212, 259 |
| abstract_inverted_index.intersoftware | 168 |
| abstract_inverted_index.measurements. | 172 |
| abstract_inverted_index.observational | 73 |
| abstract_inverted_index.respectively. | 207 |
| abstract_inverted_index.segmentation, | 31 |
| abstract_inverted_index.segmentation. | 158 |
| abstract_inverted_index.understanding | 18 |
| abstract_inverted_index.Bland–Altman | 162 |
| abstract_inverted_index.simultaneously | 262 |
| abstract_inverted_index.cross-sectional | 72 |
| abstract_inverted_index.epidemiological | 277 |
| abstract_inverted_index.deep-learning–based | 45, 103 |
| abstract_inverted_index.Intersection-over-union | 146 |
| abstract_inverted_index.intersections-over-union | 188 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5100372054 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I4210166576, https://openalex.org/I76130692 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.86018297 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |