ArticlePLoS computational biology2018
Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 69 citations in OpenAlex.
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- Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.PLoS computational biology · 2023Article
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- Disaggregation as an interaction mechanism among intestinal bacteria.Biophysical journal · 2022Article
- Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy.ACS sensors · 2022Article
- Deep learning-based image processing in optical microscopy.Biophysical reviews · 2022Review
- Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm.Multidimensional systems and signal processing · 2022Article
- Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues.Archives of computational methods in engineering : state of the art reviews · 2022Review
- The role of convolutional neural networks in scanning probe microscopy: a review.Beilstein journal of nanotechnology · 2021Review
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- Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review.Multimedia tools and applications · 2021Article
- Navigating the Light-Sheet Image Analysis Software Landscape: Concepts for Driving Cohesion From Data Acquisition to Analysis.Frontiers in cell and developmental biology · 2021Review
- COVID-19 Control by Computer Vision Approaches: A Survey.IEEE access : practical innovations, open solutions · 2020Article
- OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.PloS one · 2020Article
- Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.Frontiers in plant science · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the microscale, but bring with them larger and more complex datasets. New image processing techniques are therefore called for to analyze the resulting images in an accurate and efficient manner. Convolutional neural networks are becoming the standard for classification of objects within images due to their accuracy and generalizability compared to traditional techniques. Their application to data derived from 3D imaging, however, is relatively new and has mostly been in areas of magnetic resonance imaging and computer tomography. It remains unclear, for images of discrete cells in variable backgrounds as are commonly encountered in fluorescence microscopy, whether convolutional neural networks provide sufficient performance to warrant their adoption, especially given the challenges of human comprehension of their classification criteria and their requirements of large training datasets. We therefore applied a 3D convolutional neural network to distinguish bacteria and non-bacterial objects in 3D light sheet fluorescence microscopy images of larval zebrafish intestines. We find that the neural network is as accurate as human experts, outperforms random forest and support vector machine classifiers, and generalizes well to a different bacterial species through the use of transfer learning. We also discuss network design considerations, and describe the dependence of accuracy on dataset size and data augmentation. We provide source code, labeled data, and descriptions of our analysis pipeline to facilitate adoption of convolutional neural network analysis for three-dimensional microscopy data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.