You are here

Nat Protoc DOI:10.1038/s41596-021-00549-7

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Publication TypeJournal Article
Year of Publication2021
AuthorsDoan, M, Barnes, C, McQuin, C, Caicedo, JC, Goodman, A, Carpenter, AE, Rees, P
JournalNat Protoc
Volume16
Issue7
Pages3572-3595
Date Published2021 07
ISSN1750-2799
KeywordsDeep Learning, Image Cytometry, Software, Supervised Machine Learning, User-Computer Interface
Abstract

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.

DOI10.1038/s41596-021-00549-7
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/34145434?dopt=Abstract

Alternate JournalNat Protoc
PubMed ID34145434
Grant ListBB/P026818/1 / / RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) /
DBI 1458626 / / NSF | BIO | Division of Biological Infrastructure (DBI) /
R35 GM122547 / GM / NIGMS NIH HHS / United States