Authors
Carlos X Hernández, Mohammad M Sultan, Vijay S Pande
Publication date
2018/2/28
Journal
arXiv preprint arXiv:1802.10548
Description
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 approach, using a recently described feature pyramid network combined with a VGG-style neural network, for segmenting and subsequent counting of cells in a given microscopy image.
Total citations
2018201920202021202220231771057
Scholar articles
CX Hernández, MM Sultan, VS Pande - arXiv preprint arXiv:1802.10548, 2018