Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids

Abraham, Thomas, Barbier, Michaël, Jaensch, Steffen, Cornelissen, Frans, Vidic, Suzana, Gjerde, Kjersti, de Hoogt, Ronald, Graeser, Ralph, Gustin, Emmanuel, Chong, Yolanda T. and Smalley, Matthew J. 2016. Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids. PLoS ONE 11 (6) , e0156942. 10.1371/journal.pone.0156942

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (8MB) | Preview

Abstract

In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
European Cancer Stem Cell Research Institute (ECSCRI)
Additional Information: Matthew Smalley is part of the IMI PREDICT Consortium.
Publisher: Public Library of Science
ISSN: 1932-6203
Funders: EU Innovative Medicines Initiative
Date of First Compliant Deposit: 4 July 2017
Date of Acceptance: 23 May 2016
Last Modified: 24 Apr 2019 12:19
URI: http://orca.cf.ac.uk/id/eprint/102031

Citation Data

Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics