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Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
Author(s) -
Caicedo Juan C.,
Roth Jonathan,
Goodman Allen,
Becker Tim,
Karhohs Kyle W.,
Broisin Matthieu,
Molnar Csaba,
McQuin Claire,
Singh Shantanu,
Theis Fabian J.,
Carpenter Anne E.
Publication year - 2019
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.23863
Subject(s) - deep learning , computer science , artificial intelligence , segmentation , set (abstract data type) , task (project management) , pattern recognition (psychology) , cytometry , code (set theory) , source code , machine learning , flow cytometry , biology , management , economics , genetics , programming language , operating system
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.