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Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Caicedo, Juan C. and Roth, Jonathan and Goodman, Allen and Becker, Tim and Karhohs, Kyle W. and Broisin, Matthieu and Molnar, Csaba and McQuin, Claire and Singh, Shantanu and Theis, Fabian J. and Carpenter, Anne E. (2019) Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images. CYTOMETRY PART A, 95 (9). pp. 952-965. ISSN 1552-4922

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Abstract

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. (c) 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Item Type: Article
Uncontrolled Keywords: image analysis; fluorescence imaging; Deep learning; Nuclear segmentation; chemical screen;
Subjects: Q Science / természettudomány > QH Natural history / természetrajz > QH301 Biology / biológia > QH3011 Biochemistry / biokémia
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 21 Nov 2019 13:10
Last Modified: 21 Nov 2019 13:10
URI: http://real.mtak.hu/id/eprint/103501

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