Optimizing Voronoi-based quantifications for reaching interactive analysis of 3D localizations in the million range

Frontiers in Bioinformatics

Bioinformatics

Florian Levet¹ 

[1] CNRS, Interdisciplinary Institute for Neuroscience, IINS, UMR 5297, University of Bordeaux, Bordeaux, France;CNRS, INSERM, Bordeaux Imaging Center, BIC, UAR3420, US 4, University of Bordeaux, Bordeaux, France;

关键词: single molecule localisation microscopy (SMLM);    segmentation;    Voronoi–Delaunay tessellation;    GPU (CUDA);    clustering;   

DOI  :  10.3389/fbinf.2023.1249291

 received in 2023-06-28, accepted in 2023-07-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

Over the last decade, single-molecule localization microscopy (SMLM) has revolutionized cell biology, making it possible to monitor molecular organization and dynamics with spatial resolution of a few nanometers. Despite being a relatively recent field, SMLM has witnessed the development of dozens of analysis methods for problems as diverse as segmentation, clustering, tracking or colocalization. Among those, Voronoi-based methods have achieved a prominent position for 2D analysis as robust and efficient implementations were available for generating 2D Voronoi diagrams. Unfortunately, this was not the case for 3D Voronoi diagrams, and existing methods were therefore extremely time-consuming. In this work, we present a new hybrid CPU-GPU algorithm for the rapid generation of 3D Voronoi diagrams. Voro3D allows creating Voronoi diagrams of datasets composed of millions of localizations in minutes, making any Voronoi-based analysis method such as SR-Tesseler accessible to life scientists wanting to quantify 3D datasets. In addition, we also improve ClusterVisu, a Voronoi-based clustering method using Monte-Carlo simulations, by demonstrating that those costly simulations can be correctly approximated by a customized gamma probability distribution function.

【 授权许可】

Unknown   
Copyright © 2023 Levet.

【 图 表 】

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