B6_dapi_template

Description

This repository contains the full data set for constructing the DAPI template, the full DAPI template creation pipeline and the automatic slice segmentation.

Automatic slice segmentation

Program for automatic segmentation to a DAPI-stained coronal mouse brain slices.

How to setup the enviroment

Below is a quick guide to setup the enviroment on a UNIX (Mac and Linux) system

• To run the program, Python 3.7+ is required. You can get the newest version of python by installing Anaconda. To check the version of the python instalation typing

  python --version
  

  • Install ANTs. This is done by adding a precompiled binaries to the libary directory. The precompiled binaries for Mac/Linux is avaliable at
    https://github.com/ANTsX/ANTs/releases/tag/v2.1.0

  • Make sure to add ANTs to the path. This is done by editing the config file for the terminal shell. The config file would often be named

    ~/.bashrc
    

In the configfile add the path. This can be done by inserting

  export PATH=$PATH:usr/local/bin/ANTs:

Here the path to the ANTs registration was $/usr/local/bin/ANTs

After adding this to the config file, restart the terminal and test if the path was added correctly by typing

  which antsRegistration

The correct path should then be printed.

• if permission denied, then the file needs to be made executable - this is done using the command

  chmod +x <...>/antsRegistration
  

  • Make sure to have installed or updated all packages listed in requirements.txt. All the packages can be updated using pip. It is espicially crucial to have installed to have installed the non standard packages Nibabel and nipype. To install a a package i.e. Nibabel
    

  pip install nibabel
  

To check the version of a package

  pip freeze | grep nibabel

Prerequisites

• A high-resolution, DAPI modality TIFF image of a brain slice. Can have multiple channels.
  
• A template file (.nii or .nii.gz)
  
• A segmentation of the template file (.nii or .nii.gz)
  
  

Usage

• Use runner.py to preprocess and output a sgementation registred to the slice in a designated output folder
• Use preprocess.py to prepare input slice for automatic registration
  

Example_folder.zip

In the repository is included a zip file that contains all the files and scripts used in order to run an Example of the program. To run the example, download and unzip the folder and open the folder in a terminal window. Make sure that the enviroment is setup correct as descirbed above, and then run the shell fill by

bash runner_example.sh

Details

Detailed parameter description.

runner.py

Usage

python3 runner.py sliceloc segloc templateloc bregma [coord] outputdir

In the subfolder 'Example' a shell script is provided that runs the programs as intended with test brainslice.

Positional/Required Arguments

sliceloc	Location of the slice you wish to register (.nii or.nii.gz)
segloc	Location of file containing segmentation of the template (.nii or.nii.gz)
templateloc	Location of template file (.nii or .nii.gz)
bregma [coord]	Bregma coordinate of input slice
outputdir	Location for the output of the registration files

Optional arguments

--dapi [index]	Index of the DAPI channel in the slice, by default the DAPI channel is assumed to be the last channel

preprocess.py

Usage

python3 preprocess.py file dir -s Series --pdim [pixeldimensions]  

Positional/Required Arguments

file	Location of the file to be preprocessed (.tiff or .nd2)
dir	Directory to output preprocessed files (will output .nii files)
-s	The series to extract (If input file is single-series, use 0 for this argument)

Optional arguments

--pdim [pixeldimensions]	Dimensions of pixels, if not provided, these will be extracted from image metadata

This repository contains the full data set for constructing the DAPI template, the full DAPI template creation pipeline and the automatic slice segmentation.