• Corpus ID: 3656770

Using Deep Learning for Segmentation and Counting within Microscopy Data

@article{Hernndez2018UsingDL,
  title={Using Deep Learning for Segmentation and Counting within Microscopy Data},
  author={Carlos X. Hern{\'a}ndez and Mohammad M. Sultan and Vijay S. Pande},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.10548}
}
Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation. From basic biological questions to clinical trials, cell counts provide key quantitative feedback that drive research. Unfortunately, cell counting is most commonly a manual task and can be time-intensive. The task is made even more difficult due to overlapping cells, existence of multiple focal planes, and poor imaging quality, among other factors. Here, we describe a convolutional neural network… 
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FASTER R-CNN for cell counting in low contrast microscopic images

  • A. UkaAleks TareX. PolisiIna Panci
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    2020 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)
  • 2020
A convolutional neural network approach is described and test, employing CNN with region proposal called Faster-RCNN for cell counting in low contrast microscopic images, achieving an average precision of 79%.

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