Approximating Cellular Densities
version 0.1 - this repository was last updated on 10/2/2018
You can find further details about how we apply the methods in this repo to quantify neocortical layers of mouse brains in the following papers. If you use any part of the code or data in this repo, please cite the first paper. Please direct any questions to Theodore J. LaGrow at tlagrow(at)gatech(dot)edu or Eva Dyer at evadyer{at}gatech{dot}edu.
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T.J. LaGrow, M. Moore, J.A. Prasad, A. Webber, M.A. Davenport, E.L. Dyer, "Cytoarchitecture and Layer Estimation in High-Resolution Neuroanatomical Images," in review, July 2018.
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T. J. LaGrow, M. G. Moore, J. A. Prasad, M. A. Davenport, and E. L. Dyer, “Approximating cellular densities from high-resolution neuroanatomical imaging data,” IEEE Engineering in Medicine and Biology Society Annual Conference (EMBC), July 2018. [Paper]
This repository is designed to hold demonstration code for the ArCaDe pipeline. The three main folders (patch_extraction/, cell_detection/, and rate_estimation/) hold the scripts needed to perform each step. Figures produced by the demo scripts are located in their respected folders.
The data utilized in this repo can be found at: https://drive.google.com/drive/folders/1p6NiX26l39gscg6nYFltlT1fZ7nwZqpG?usp=sharing.
Remember to include this data folder into the path of your working environment before running any of the demos.
- Matlab version >= 2007
In order to download ArCaDe folder, either download zip file from the git repository directly or use git to clone the
repository using the command:
git clone https://github.com/nerdslab/arcade
You can run any demo of the three steps in the ArCaDe pipeline independently each in their designated folders with the following scripts: script_patch_extraction.m, script_cell_detection.m, and script_rate_estimation.m.
- The MIT License (MIT) Copyright (c) 2018 Theodore J. LaGrow, Michael G. Moore, Judy A. Prasad, Mark A. Davenport, and Eva L. Dyer