Unveiling the third dimension in morphometry with automated quantitative volumetric computations

Abstract As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly...

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Autores principales:	Lawrence R. Frank, Timothy B. Rowe, Doug M. Boyer, Lawrence M. Witmer, Vitaly L. Galinsky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a7425c1ef6414657a1d0213d306738f4
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spelling	oai:doaj.org-article:a7425c1ef6414657a1d0213d306738f42021-12-02T16:14:02ZUnveiling the third dimension in morphometry with automated quantitative volumetric computations10.1038/s41598-021-93490-42045-2322https://doaj.org/article/a7425c1ef6414657a1d0213d306738f42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93490-4https://doaj.org/toc/2045-2322Abstract As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition (SWD), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways (SYMREG), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data (SAPID) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems.Lawrence R. FrankTimothy B. RoweDoug M. BoyerLawrence M. WitmerVitaly L. GalinskyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-23 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lawrence R. Frank Timothy B. Rowe Doug M. Boyer Lawrence M. Witmer Vitaly L. Galinsky Unveiling the third dimension in morphometry with automated quantitative volumetric computations
description	Abstract As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition (SWD), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways (SYMREG), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data (SAPID) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems.
format	article
author	Lawrence R. Frank Timothy B. Rowe Doug M. Boyer Lawrence M. Witmer Vitaly L. Galinsky
author_facet	Lawrence R. Frank Timothy B. Rowe Doug M. Boyer Lawrence M. Witmer Vitaly L. Galinsky
author_sort	Lawrence R. Frank
title	Unveiling the third dimension in morphometry with automated quantitative volumetric computations
title_short	Unveiling the third dimension in morphometry with automated quantitative volumetric computations
title_full	Unveiling the third dimension in morphometry with automated quantitative volumetric computations
title_fullStr	Unveiling the third dimension in morphometry with automated quantitative volumetric computations
title_full_unstemmed	Unveiling the third dimension in morphometry with automated quantitative volumetric computations
title_sort	unveiling the third dimension in morphometry with automated quantitative volumetric computations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a7425c1ef6414657a1d0213d306738f4
work_keys_str_mv	AT lawrencerfrank unveilingthethirddimensioninmorphometrywithautomatedquantitativevolumetriccomputations AT timothybrowe unveilingthethirddimensioninmorphometrywithautomatedquantitativevolumetriccomputations AT dougmboyer unveilingthethirddimensioninmorphometrywithautomatedquantitativevolumetriccomputations AT lawrencemwitmer unveilingthethirddimensioninmorphometrywithautomatedquantitativevolumetriccomputations AT vitalylgalinsky unveilingthethirddimensioninmorphometrywithautomatedquantitativevolumetriccomputations
_version_	1718384367898722304

Unveiling the third dimension in morphometry with automated quantitative volumetric computations

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