Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations
Marco Agus, Maria Veloz Castillo, Javier F. Garnica Molina, Enrico Gobbetti, Heikki Lehvaslaiho, Alex Morales Tapia, Pierre Magistretti, Markus Hadwiger, and Corrado Calí
2019
Abstract
Shape analysis of cell nuclei is becoming increasingly important in biology and medicine. Recent results have identified that large variability in shape and size of nuclei has an important impact on many biological processes. Current analysis techniques involve automatic methods for detection and segmentation of histology and microscopy images, but are mostly performed in 2D. Methods for 3D shape analysis, made possible by emerging acquisition methods capable to provide nanometric-scale 3D reconstructions, are still at an early stage, and often assume a simple spherical shape. We introduce here a framework for analyzing 3D nanoscale reconstructions of nuclei of brain cells (mostly neurons), obtained by semiautomatic segmentation of electron micrographs. Our method considers two parametric representations: the first one customizes the implicit hyperquadrics formulation and it is particularly suited for convex shapes, while the latter considers a spherical harmonics decomposition of the explicit radial representation. Point clouds of nuclear envelopes, extracted from image data, are fitted to the parameterized models which are then used for performing statistical analysis and shape comparisons. We report on the analysis of a collection of 121 nuclei of brain cells obtained from the somatosensory cortex of a juvenile rat.
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Marco Agus, Maria Veloz Castillo, Javier F. Garnica Molina, Enrico Gobbetti, Heikki Lehvaslaiho, Alex Morales Tapia, Pierre Magistretti, Markus Hadwiger, and Corrado Calí. Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations. Computers & Graphics, 2019. DOI: 10.1016/j.cagx.2019.100004.
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Bibtex citation record
@Article{Agus:2019:SA3, author = {Marco Agus and Maria {Veloz Castillo} and Javier F. {Garnica Molina} and Enrico Gobbetti and Heikki {Lehvaslaiho} and Alex {Morales Tapia} and Pierre Magistretti and Markus Hadwiger and Corrado {Cal\'i}}, title = {Shape analysis of {3D} nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations}, journal = {Computers \& Graphics}, pages = { }, year = {2019}, abstract = { Shape analysis of cell nuclei is becoming increasingly important in biology and medicine. Recent results have identified that large variability in shape and size of nuclei has an important impact on many biological processes. Current analysis techniques involve automatic methods for detection and segmentation of histology and microscopy images, but are mostly performed in 2D. Methods for 3D shape analysis, made possible by emerging acquisition methods capable to provide nanometric-scale 3D reconstructions, are still at an early stage, and often assume a simple spherical shape. We introduce here a framework for analyzing 3D nanoscale reconstructions of nuclei of brain cells (mostly neurons), obtained by semiautomatic segmentation of electron micrographs. Our method considers two parametric representations: the first one customizes the implicit \textit{hyperquadrics} formulation and it is particularly suited for convex shapes, while the latter considers a \textit{spherical harmonics} decomposition of the explicit radial representation. Point clouds of nuclear envelopes, extracted from image data, are fitted to the parameterized models which are then used for performing statistical analysis and shape comparisons. We report on the analysis of a collection of 121 nuclei of brain cells obtained from the somatosensory cortex of a juvenile rat. }, doi = {10.1016/j.cagx.2019.100004}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Agus:2019:SA3'}, }
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