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. 2016 Oct 14;18:73. doi: 10.1186/s12968-016-0291-9

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© The Author(s). 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 2 — The statistical analysis framework to analyse the anatomical differences between ARVC patients and asymptomatic control subjects by considering the mean (average) anatomy as the model of interest and describing all other subjects as a deformation of this model. The subject-specific deformations can be analysed to extract dominant anatomical features in the population (modes), as well as studying the relationship between the anatomical features and clinical diagnostic parameters. A real-world example is shown by considering the geography of the earth, representing this in 3D with a scale model (a globe), projecting this to 2D (a map), and extracting dominant geographical features such as altitude, to correlate with an external geographical parameter (temperature for example)