BrainCellCount

BrainCellCount is an open-source, semi-automatically Slice-to-volume Brain Aligner, to count cells via mapping mouse brain regions of interests (ROIs) between the studying brain and the Allen mouse brain atlas CCFv3.

Install

BrainCellCount requires ANTs registration toolkit to be installed.

BrainCellCount uses Python 3 and depends on python packages such as numpy, scipy, scikit-image, ....

To create a new conda environment:

    conda create -n braincellcount python=3.8
    conda activate braincellcount

Install required python packages:

   pip install -r requirements.txt

Get Started

Here is a basic guide that introduces BrainCellCount and its functionalities.

• Data preparation

  • put your 2D slice images (*.tif) into a file "filelist.txt":

    for i in {1..15}_{1..10}.tif; do echo $i >> filelist.txt; done

  • downsample all images into the same size and set image boundaries with zeros:

    n=100; while read file; do if [ -f $file ]; then n=$((n+1)); python zeroBoundary_hsv6.py $file im${n}.nii.gz; fi; done < filelist.txt

• Map 2D slice images to CCFv3

  • select ~10 - 15 "key" 2D slice images and find matched the slice of CCFv3, save to "npoints.txt"

  • map the rest 2D slice images to CCFv3 using scipy.optimize.curve_fit, save all best matched pair images' z positions to "ready2reg.txt"

  • ensure to be registered the images are under "./sampled" and template images are under "./ccf", create an output folder ".reg"

  • proofread and label the slices when their rotations are more than 90 degrees to "rotations.txt" with 90/180/270, run:

    sh batchprocess.sh

• Count cells in each interested brain area

  • put cell segmentation images into "binaryimage" folder

  • put your ROIs into a CSV file "interestedregions.csv", e.g. ROIs in this study

  • to obtain the cell counting result, run:

    python analysis.py cellseg.tif alignedannotations.nii.gz fig.eps

Citation

To cite this software package, please reference, as appropriate:

Rong Gong , Shengjin Xu, Ann Hermundstad , Yang Yu, Scott M. Sternson. Double negative feedback controls palatability in convergent hunger and thirst circuits. Cell. 2020.