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Correction: Deep-LUMEN assay – human lung epithelial spheroid classification from brightfield images using deep learning
Lyan
Abdul
a,
Shravanthi
Rajasekar
b,
Dawn S. Y.
Lin
b,
Sibi
Venkatasubramania Raja
c,
Alexander
Sotra
b,
Yuhang
Feng
c,
Amy
Liu
c and
Boyang
Zhang
*ab
aSchool of Biomedical Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
bDepartment of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada. E-mail: zhangb97@mcmaster.ca
cFaculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada
First published on 17th December 2020
Abstract
Correction for ‘Deep-LUMEN assay – human lung epithelial spheroid classification from brightfield images using deep learning’ by Lyan Abdul et al., Lab Chip, 2020, DOI: 10.1039/d0lc01010c.
An incorrect version of Fig. 1 was included in the article at proofing stage. The correct version is shown herein.
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| Fig. 1 Differential formation of hollow lung alveolar spheroids. a, Illustration of lung epithelial cells proliferating and assembling into either hollow or solid spheroids in a 3D matrix. b, Tissue culture setup where 25 μL of Matrigel® embedded with cells are cast in standard 384-well plates. c, Acquired z-stack transmitted-light images. d, Example scenarios of lung spheroids seen from the collected images. Corresponding fluorescent images stained for F-actin (red) and DAPI (blue) of lung spheroids with or without a lumen (representative images from n = 6 samples). Scale bar, 50 μm. e, z-Stack acquisition allows for spheroid morphology assessment throughout entire hydrogel. Spheroids on different focal planes were detected with developed deep-LUMEN algorithm from z-stack images and then labeled with different colors for visualization. | ||
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
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