Open-source deep-learning software for bioimage segmentation
| Autor: | Anne E. Carpenter, Bin Li, Pearl V. Ryder, Kevin W. Eliceiri, Alice Lucas, Beth A. Cimini |
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| Rok vydání: | 2021 |
| Předmět: |
Technical Perspective
Biology Field (computer science) 03 medical and health sciences Deep Learning 0302 clinical medicine Software Image Processing Computer-Assisted Humans Web application Segmentation Molecular Biology 030304 developmental biology Microscopy 0303 health sciences business.industry Deep learning Computational Biology Cell Biology Image segmentation Data science Open source Workflow Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Molecular Biology of the Cell |
| ISSN: | 1939-4586 1059-1524 |
| DOI: | 10.1091/mbc.e20-10-0660 |
| Popis: | Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis. |
| Databáze: | OpenAIRE |
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