SimpylCellCounter (SCC) is a fast, robust, and automated method for quantifying cells in brain tissue. SCC is a Python-based algorithm that utilizes the open-source computer vision package OpenCV. SCC achieves high speeds by relying mainly on simple computer vision techniques such as binary thresholding and noise filtering.
SCC is also highly-customizable, allowing the user to alter nearly every parameter. These parameters include threshold values for adaptive binary thresholding and cell discrimination, noise filtering levels, as well as the radius, area, and circularity of cells to-be-counted.
Lastly, SCC requires minimal knowledge of coding methodologies and includes a full graphical user interface (GUI). Browsing for files and folders is similar to most computer applications, and modification of source code is never required.
SCC 2.0 has been released! New documentation is currently only available on this repository.
To learn more about older versions of SCC, read the paper here: https://www.nature.com/articles/s41598-020-68138-4
SCC 1.1 and documentation thereof are also available on the v1.1 branch of this repository.
Click here to visit the SimpylCellCounter Wiki!
Project Supervisor
Amy Arguello, PhD - arguell5@msu.edu
SCC 2.0 Lead Developer
Chris Reeves - reevesc7@msu.edu
SCC 1.1 Lead Developer
Aneesh Bal - aneesh.s.bal@gmail.com