Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
Please always quote using this URN: urn:nbn:de:bvb:20-opus-357286
- 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 modelBioimages 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.…
Author: | Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft, Philip Tovote, Robert Blum, Christoph M. Flath |
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URN: | urn:nbn:de:bvb:20-opus-357286 |
Document Type: | Journal article |
Faculties: | Wirtschaftswissenschaftliche Fakultät / Betriebswirtschaftliches Institut |
Medizinische Fakultät / Institut für Klinische Neurobiologie | |
Medizinische Fakultät / Neurologische Klinik und Poliklinik | |
Language: | English |
Parent Title (English): | Nature Communications |
Year of Completion: | 2023 |
Volume: | 14 |
Article Number: | 1679 |
Source: | Nature Communications (2023) 14:1679. https://doi.org/10.1038/s41467-023-36960-9 |
DOI: | https://doi.org/10.1038/s41467-023-36960-9 |
Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit | |
Tag: | machine learning; microscopy; quality control; software |
Release Date: | 2024/04/30 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |