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Caicedo, J.C.* ; Roth, J. ; Goodman, A.* ; Becker, T.* ; Karhohs, K.W.* ; Broisin, M.* ; Molnar, C.* ; McQuin, C.* ; Singh, S.* ; Theis, F.J. ; Carpenter, A.E.*

Evaluation of deep learning strategies for nucleus segmentation in fluorescence images.

Cytometry A 95, 952-965 (2019)
Publ. Version/Full Text Postprint Research data DOI
Open Access Gold (Paid Option)
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Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Chemical Screen ; Deep Learning ; Fluorescence Imaging ; Image Analysis ; Nuclear Segmentation; Micronuclei
ISSN (print) / ISBN 1552-4922
e-ISSN 1552-4930
Journal Cytometry Part A
Quellenangaben Volume: 95, Issue: 9, Pages: 952-965 Article Number: , Supplement: ,
Publisher Wiley
Publishing Place Hoboken, NJ
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)