ArticlePloS one2019
IMACEL: A cloud-based bioimage analysis platform for morphological analysis and image classification.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2021Article
- A two-stage method for automated detection of ring-like endosomes in fluorescent microscopy images.PloS one · 2019Article
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Authors and funding
7 authors.
Funding
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Abstract
Automated quantitative image analysis is essential for all fields of life science research. Although several software programs and algorithms have been developed for bioimage processing, an advanced knowledge of image processing techniques and high-performance computing resources are required to use them. Hence, we developed a cloud-based image analysis platform called IMACEL, which comprises morphological analysis and machine learning-based image classification. The unique click-based user interface of IMACEL's morphological analysis platform enables researchers with limited resources to evaluate particles rapidly and quantitatively without prior knowledge of image processing. Because all the image processing and machine learning algorithms are performed on high-performance virtual machines, users can access the same analytical environment from anywhere. A validation study of the morphological analysis and image classification of IMACEL was performed. The results indicate that this platform is an accessible and potentially powerful tool for the quantitative evaluation of bioimages that will lower the barriers to life science research.
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Registered trials
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