Interactive machine learning for fast and robust cell profiling

Laux, L., Cutiongco, M. F.A. , Gadegaard, N. and Jensen, B. S. (2020) Interactive machine learning for fast and robust cell profiling. PLoS ONE, 15(9), e0237972. (doi: 10.1371/journal.pone.0237972) (PMID:32915784) (PMCID:PMC7485821)

Abstract

Automated profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases and dependent on user experience. Here, we use interactive machine learning to identify the optimum cell profiling configuration that maximises quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. We use Bayesian optimisation, an established machine learning algorithm, to learn from this information and automatically recommend the next configuration to examine with the aim of maximising the quality of the processing or analysis. Compared to existing interactive machine learning tools that require domain expertise for per-class or per-pixel annotations, we rely on users’ explicit assessment of output quality of the cell profiling task at hand. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task using the standard software CellProfiler. Our toolkit enabled rapid optimisation of an object segmentation pipeline, increasing the quality of object segmentation over a pipeline optimised through trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning approach can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cutiongco, Ms Marie and Jensen, Dr Bjorn and Gadegaard, Professor Nikolaj

Creator Roles

Cutiongco, M. F.A.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
Gadegaard, N.: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review and editing
Jensen, B. S.: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review and editing

• Authors: Laux, L., Cutiongco, M. F.A., Gadegaard, N., and Jensen, B. S.
• College/School: College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: PLoS ONE
• Publisher: Public Library of Science
• ISSN: 1932-6203
• ISSN (Online): 1932-6203
• Copyright Holders: Copyright © 2020 Laux et al.
• First Published: First published in PLoS ONE 15(9): e0237972
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172025		FAKIR: Focal Adhesion Kinetics In nanosurface Recognition	Nikolaj Gadegaard	European Research Council (ERC)	648892	ENG - Biomedical Engineering
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science