Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2

TL;DR

deepflash2 is a deep learning tool designed for reliable bioimage segmentation, effectively handling ambiguous data through multi-expert annotations and quality assurance, with a user-friendly interface and high performance.

Contribution

It introduces deepflash2, a novel deep learning framework that improves bioimage segmentation by incorporating multi-expert annotations and quality control within an accessible GUI.

Findings

Achieves state-of-the-art segmentation accuracy.

Handles ambiguous bioimages effectively.

Uses minimal computational resources.

Abstract

We present deepflash2, a deep learning solution that facilitates the objective and reliable segmentation of ambiguous bioimages through multi-expert annotations and integrated quality assurance. Thereby, deepflash2 addresses typical challenges that arise during training, evaluation, and application of deep learning models in bioimaging. The tool is embedded in an easy-to-use graphical user interface and offers best-in-class predictive performance for semantic and instance segmentation under economical usage of computational resources.