Deep learning-enabled segmentation of ambiguous bioimages with deepflash2

Matthias Griebel (), Dennis Segebarth, Nikolai Stein, Nina Schukraft, Philip Tovote, Robert Blum and Christoph M. Flath ()
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Matthias Griebel: University of Würzburg
Dennis Segebarth: University Hospital Würzburg
Nikolai Stein: University of Würzburg
Nina Schukraft: University Hospital Würzburg
Philip Tovote: University Hospital Würzburg
Robert Blum: University Hospital Würzburg
Christoph M. Flath: University of Würzburg

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.

Date: 2023
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DOI: 10.1038/s41467-023-36960-9

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