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On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.

Dennis Segebarth¹, Matthias Griebel², Nikolai Stein²

¹Institute of Clinical Neurobiology, University Hospital Würzburg, Würzburg, Germany.

|October 19, 2020

Summary

This summary is machine-generated.

Automating bioimage analysis with deep learning (DL) requires objective annotations. This study shows that using multiple annotators for ground truth estimation and training model ensembles improves reliability and validity in fluorescent image analysis.

Keywords:

bioimage informatics computational biology deep learning fluorescence microscopy mouse neuroscience objectivity reproducibility systems biology validity zebrafish

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Area of Science:

Life Sciences
Bioimage Analysis
Deep Learning Applications

Background:

Bioimage analysis of fluorescent labels is crucial in life sciences.
Deep learning (DL) automates manual image analysis but relies on annotated data.
Subjectivity in annotating low signal-to-noise fluorescent features can lead to biased DL models.

Purpose of the Study:

To evaluate an integrated pipeline for DL-based bioimage analysis.
To mitigate risks associated with subjective manual annotations.
To establish objectivity, reliability, and validity in DL model training for bioimage analysis.

Main Methods:

Comparison of different DL-based analysis approaches.
Utilized data from two model organisms (mice, zebrafish) across five laboratories.
Implemented ground truth estimation using multiple human annotators and trained model ensembles.

Main Results:

Ground truth estimation from multiple annotators enhanced objectivity in fluorescent feature annotations.
Ensembles of DL models trained on estimated ground truth demonstrated reliability and validity.
The integrated pipeline effectively addressed challenges in DL-based bioimage analysis.

Conclusions:

Multiple human annotators are key to establishing objective ground truth for bioimage analysis.
Model ensembles trained on consensus ground truth ensure reproducible and valid DL-based analyses.
Provides guidelines for robust and reproducible deep learning applications in bioimage analysis.