TY  - JOUR
A1  - Griebel, Matthias
A1  - Segebarth, Dennis
A1  - Stein, Nikolai
A1  - Schukraft, Nina
A1  - Tovote, Philip
A1  - Blum, Robert
A1  - Flath, Christoph M.
T1  - Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
T2  - Nature Communications
N2  - 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.
KW  - machine learning
KW  - microscopy
KW  - quality control
KW  - software
Y1  - 2023
UR  - https://opus.bibliothek.uni-wuerzburg.de/opus4-wuerzburg/frontdoor/index/index/docId/35728
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-357286
VL  - 14
ER  -