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  • PrestoCell: A persistence-based clustering approach for rapid and robust segmentation of cellular morphology in three-dimensional data
Title: PrestoCell: A persistence-based clustering approach for rapid and robust segmentation of cellular morphology in three-dimensional data
Light microscopy methods have continued to advance allowing for unprecedented analysis of various cell types in tissues including the brain. Although the functional state of some cell types such as microglia can be determined by morphometric analysis, techniques to perform robust, quick, and accurate measurements have not kept pace with the amount of imaging data that can now be generated. Most of these image segmentation tools are further burdened by an inability to assess structures in three-dimensions. Despite the rise of machine learning techniques, the nature of some biological structures prevents the training of several current day implementations. Here we present PrestoCell, a novel use of persistence-based clustering to segment cells in light microscopy images, as a customized Python-based tool that leverages the free multidimensional image viewer Napari. In evaluating and comparing PrestoCell to several existing tools, including 3DMorph, Omipose, and Imaris, we demonstrate that PrestoCell produces image segmentations that rival these solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another to increase accuracy. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.  more » « less
Award ID(s):
1934568
PAR ID:
10555605
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Neueder, Andreas
Publisher / Repository:
PLOS
Date Published:
Journal Name:
PLOS ONE
Volume:
19
Issue:
2
ISSN:
1932-6203
Page Range / eLocation ID:
e0299006
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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