Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI Article Swipe
Jiasong Chen
,
Linchen Qian
,
Linhai Ma
,
Timur Urakov
,
Weiyong Gu
,
Liang Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.04.02.587810
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.04.02.587810
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (discs and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we evaluated 15 existing DNN models for lumbar spine MR image segmentation. We introduced a new data augmentation technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. The 15 image segmentation models are evaluated on our private in-house dataset and the public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and 95% Hausdorff Distance. The SSMSpine dataset are available at https://github.com/jiasongchen/SSMSpine .
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.04.02.587810
- https://www.biorxiv.org/content/biorxiv/early/2024/04/03/2024.04.02.587810.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393903108
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4393903108Canonical identifier for this work in OpenAlex
- DOI
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- Title
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Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRIWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-03Full publication date if available
- Authors
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Jiasong Chen, Linchen Qian, Linhai Ma, Timur Urakov, Weiyong Gu, Liang LiangList of authors in order
- Landing page
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https://doi.org/10.1101/2024.04.02.587810Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2024/04/03/2024.04.02.587810.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/04/03/2024.04.02.587810.full.pdfDirect OA link when available
- Concepts
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Segmentation, Artificial intelligence, Computer science, Hausdorff distance, Artificial neural network, Sørensen–Dice coefficient, Similarity (geometry), Deep learning, Lumbar spine, Lumbar, Image segmentation, Pattern recognition (psychology), Image (mathematics), Computer vision, Medicine, Radiology, SurgeryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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72Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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