Title | Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes |
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Authors | Thanaj, M., Basty, N., Liu, Y., Cule, M., Sorokin, E., Thomas, E.L., Bell, J.D. and Whitcher, B. |
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Type | Conference paper |
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Abstract | Image-derived phenotypes of abdominal organs from magnetic resonance imaging reveal variations in volume and shape and may be used to model changes in a normal versus pathological organ and improve diagnosis. Computational atlases of anatomical organs provide many advantages in quantifying and modeling differences in shape and size of organs for population imaging studies. Here we made use of liver segmentations derived from Dixon MRI for 2,730 UK Biobank participants to create 3D liver meshes. We computed the signed distances between a reference and subject-specific meshes to define the surface-to-surface (S2S) phenotype. We employed mass univariate regression analysis to compare the S2S values from the liver meshes to image-derived phenotypes specific to the liver, such as proton density fat fraction and iron concentration while adjusting for age, sex, ethnicity, body mass index and waist-to-hip ratio. Vertex-based associations in the 3D liver mesh were extracted and threshold-free cluster enhancement was applied to improve the sensitivity and stability of the statistical parametric maps. Our findings show that the 3D liver meshes are a robust method for modeling the association between anatomical, anthropometric, and phenotypic variations across the liver. This approach may be readily applied to different clinical conditions as well as extended to other abdominal organs in a larger population. |
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Keywords | Registration |
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| Surface-to-surface |
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| Morphology |
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| Magnetic resonance imaging |
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Year | 2021 |
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Conference | Annual Conference on Medical Image Understanding and Analysis |
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Publisher | Springer |
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Accepted author manuscript | File Access Level Open (open metadata and files) |
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Publication dates |
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Published online | 06 Jul 2021 |
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Journal | Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science |
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Journal citation | 12722, pp. 165-176 |
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ISSN | 0302-9743 |
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ISBN | 9783030804329 |
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Funder | Calico Life Sciences LLC |
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Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-80432-9_13 |
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Web address (URL) of conference proceedings | https://link.springer.com/chapter/10.1007/978-3-030-80432-9_13#citeas |
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