Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images. Issue 9 (16th July 2019)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Cytometry. Volume 95:Issue 9(2019)
• Page Start: 952
• Page End: 965
• Publication Date: 2019-07-16
• Subjects: fluorescence imaging -- image analysis -- deep learning -- nuclear segmentation -- chemical screen; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.23863
• Languages: English
• ISSNs: 1552-4922

Abstract

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