Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)


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.


Registration Surface-to-surface Morphology Magnetic resonance imaging 



This research has been conducted using the UK Biobank Resource under Application Number ‘44584’ and was funded by Calico Life Sciences LLC.


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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Research Centre for Optimal Health, School of Life SciencesUniversity of WestminsterLondonUK
  2. 2.Calico Life Sciences LLCSouth San FranciscoUSA

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