InShaDe: Invariant Shape Descriptors for Visual Analysis of Histology 2D Cellular and Nuclear Shapes

Abstract
We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from histology images. The framework is based on a novel shape descriptor of closed contours relying on a geodesically uniform resampling of discrete curves to allow for discrete differential-geometry-based computation of unsigned curvature at vertices and edges. Our descriptor is, by design, invariant under translation, rotation and parameterization. Moreover, it additionally offers the option for uniform-scale-invariance. The optional scale-invariance is achieved by scaling features to z-scores, while invariance under parameterization shifts is achieved by using elliptic Fourier analysis (EFA) on the resulting curvature vectors. These invariant shape descriptors provide an embedding into a fixed-dimensional feature space that can be utilized for various applications: (i) as input features for deep and shallow learning techniques; (ii) as input for dimension reduction schemes for providing a visual reference for clustering collection of shapes. The capabilities of the proposed framework are demonstrated in the context of visual analysis and unsupervised classification of histology images.
Description

        
@inproceedings{
10.2312:vcbm.20201173
, booktitle = {
Eurographics Workshop on Visual Computing for Biology and Medicine
}, editor = {
Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
}, title = {{
InShaDe: Invariant Shape Descriptors for Visual Analysis of Histology 2D Cellular and Nuclear Shapes
}}, author = {
Agus, Marco
and
Al-Thelaya, Khaled
and
Cali, Corrado
and
Boido, Marina M.
and
Yang, Yin
and
Pintore, Giovanni
and
Gobbetti, Enrico
and
Schneider, Jens
}, year = {
2020
}, publisher = {
The Eurographics Association
}, ISSN = {
2070-5786
}, ISBN = {
978-3-03868-109-0
}, DOI = {
10.2312/vcbm.20201173
} }
Citation