DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Jean-Baptiste Lugagne, Haonan Lin and Mary J Dunlop

PLOS Computational Biology, 2020, vol. 16, issue 4, 1-18

Abstract: Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007673

DOI: 10.1371/journal.pcbi.1007673

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