Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations

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.

@Article{AVGGLMMHC19,
  author       = {Agus, M. and Veloz Castillo, M. and Garnica Molina, J. and Gobbetti, E. and Lehvaslaiho, H. and Morales, T. and Magistretti, P. and Hadwiger, M. and Cali, C.},
  title        = {Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations},
  journal      = {Computers \& Graphics},
  year         = {2019},
  keywords     = {Shape analysis, Nanoscale cell reconstruction, Nuclear envelopes, Cell classification},
  url          = {https://publications.crs4.it/pubdocs/2019/AVGGLMMHC19},
}