ArticleThe Journal of cell biology2020
Object detection networks and augmented reality for cellular detection in fluorescence microscopy.
Article in The Journal of cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains.Cell reports methods · 2023Article
- Event-driven acquisition for content-enriched microscopy.Nature methods · 2022Article
- DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.Communications biology · 2022Article
- A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.Scientific reports · 2022Article
- Detection and Recognition of Pollen Grains in Multilabel Microscopic Images.Sensors (Basel, Switzerland) · 2022Article
- Python-Microscope - a new open-source Python library for the control of microscopes.Journal of cell science · 2021Article
- Deep learning for bioimage analysis in developmental biology.Development (Cambridge, England) · 2021Review
- Data science in cell imaging.Journal of cell science · 2021Review
- Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.PloS one · 2021Article
- Developing open-source software for bioimage analysis: opportunities and challenges.F1000Research · 2021Article
- Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.Scientific reports · 2020Article
- Multi-template matching: a versatile tool for object-localization in microscopy images.BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.
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Registered trials
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