Using Deep Learning for Segmentation and Counting within Microscopy Data

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. Th...

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Bibliographic Details
Published in:arXiv.org 2018-02
Main Authors: Hernández, Carlos X, Sultan, Mohammad M, Pande, Vijay S
Format: Article
Language:English
Subjects:
Artificial neural networks
Counting
Deep learning
Image segmentation
Medical research
Microscopy
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422