ArticleNature communications2023
Deep learning-enabled segmentation of ambiguous bioimages with deepflash2.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Vestibular nucleus stimulation for ameliorating locomotor dynamics in a Parkinsonian mouse model.Nature communications · 2026Article
- TrkA abundance is increased in cutaneous nerves in bortezomib-induced neuropathy.Brain pathology (Zurich, Switzerland) · 2026Article
- CalDiff: Calibrating Uncertainty and Accessing Reliability of Diffusion Models for Trustworthy Lesion Segmentation.IEEE journal of biomedical and health informatics · 2026Article
- Mapping Satellite Glial Cell Heterogeneity Reveals Distinct Spatial Organization and Implies Functional Diversity in the Dorsal Root Ganglion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Age-dependent removal of Atg9-containing vesicle accumulations in motoneuron disease models by physical exercise.Translational neurodegeneration · 2025Article
- Human dorsal root ganglia are either preserved or completely lost after deafferentation by brachial plexus injury.British journal of anaesthesia · 2024Article
- Bone metabolism in complex regional pain syndrome.Pain reports · 2024Article
- Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin.Scientific reports · 2024Article
- Plekhg5 controls the unconventional secretion of Sod1 by presynaptic secretory autophagy.Nature communications · 2024Article
- Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.Cell structure and function · 2024Article
- Deep learning-enabled segmentation of ambiguous bioimages with deepflash2.Nature communications · 2023Article
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Authors and funding
7 authors.
Funding
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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.
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Registered trials
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