Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography Article Swipe
Mary K. Montgomery
,
Chong Duan
,
Lisa K. Manzuk
,
Stephanie T. Chang
,
Aiyana Cubias
,
Sonja N. Brun
,
Anand Giddabasappa
,
Ziyue Karen Jiang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.tranon.2023.101833
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.tranon.2023.101833
Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r2=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r2=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r2=0.98, DSC=0.78) performed within inter-reader variability (r2=0.79, DSC=0.69) and close to intra-reader variability (r2=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.tranon.2023.101833
- OA Status
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- Cited By
- 5
- References
- 45
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390031542Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.tranon.2023.101833Digital Object Identifier
- Title
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Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomographyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-20Full publication date if available
- Authors
-
Mary K. Montgomery, Chong Duan, Lisa K. Manzuk, Stephanie T. Chang, Aiyana Cubias, Sonja N. Brun, Anand Giddabasappa, Ziyue Karen JiangList of authors in order
- Landing page
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https://doi.org/10.1016/j.tranon.2023.101833Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.tranon.2023.101833Direct OA link when available
- Concepts
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Segmentation, Computed tomography, Artificial intelligence, Lung, Lung cancer, Medicine, Human lung, Computer science, Deep learning, Pathology, Radiology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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