Automatic segmentation and quantification of filamentous structures in electron tomography

Authors:
Leandro A. Loss;George Bebis;Hang Chang;Manfred Auer;Purbasha Sarkar;Bahram Parvin
Affiliations:
Lawrence Berkeley Nat Lab;University of Nevada, Reno;Lawrence Berkeley Nat Lab;Univ of California, Berkeley;Univ of California, Berkeley;Lawrence Berkeley Nat Lab
Venue:
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2012

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Abstract

Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.