Work

Deep Learning Strategies for Differential Expansion Microscopy

Year: 2019

Type: preprint

Abstract: Differential expansion microscopy (DiExM) achieves greater than 500-fold volumetric expansion of biological specimens without loss of cellular antigens. The anisotropic character of this expansion, which affects tissues, cells, organelles and even sub-organelle features, requires the application of novel machine learning approaches to extract accurate and meaningful morphological and biochemical information from DiExM images. Here we describe current strategies to achieve this goal.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/743682

Authors: Domenico L. Gatti, Suzan Arslanturk, Sinan Lal, Bhanu P. Jena

Institution: Wayne State University

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, Division of Chemical, Bioengineering, Environmental, and Transport Systems

Awards: G3808287275, Elucidation of Membrane Fusion Mechanisms Using a Combined Simulation and Experimental Approach, UNDERSTANDING MEMBRANE FUSION A BIOENGINEERING APPROACH, G582628961