# Description of DAPI template
This repository contains a three-dimensional population-based average atlas, the DAPI template, of the C57BL/6 mouse brain stained with the commonly employed fluorescence nuclear stain DAPI. The [DAPI template](./data/dapi_template.nii.gz), [accompanying segmentation](./data/dapi_template_segmentation_full.nii.gz), and a [simplified segmentation](./data/dapi_template_segmentation_simple.nii.gz) can be found as nifti files in ```data/```. Moreover, the repository contains all the raw data (```data/mice/```) and the full code base (```template_creation_pipieline/```) which was used in the construction of the template.

The DAPI template, is constructued from consecutive coronal brain slices of 12 male mice aged between 10-11 weeks. Each mouse brain is first reconstructed into a three-dimensional volume and then an iterative averaging process of these reconstructed brain volumes was employed to yield the final population-based average.

The repository also includes an automatic segmentation/spatial normalization pipeline (```automatic_segmentation_program/```) for novel coronal slices described below.

You can also find a [guide](#overlay-with-allenccf) on how to visualize the AllenCCF on top of the DAPI template.

# Automatic slice segmentation

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

**Getting started**
 - [Setup your environment](#setting-up-the-environment) with the correct python version, python packages, and Advanced Normalization Tools (ANTs).
 -  Download the and unzip the [example folder](./Example_folder.zip).
 - In a terminal window, navigate into the unzipped example folder.
 - Run the runner_example.sh with the command ```bash runner_example.sh```
 - The automatically created segmentation can be found in ```Brain_example/Segmentation.nii```.
 - To visualize the segmentation, open one of the channels ```Brain_example/im_c2.nii``` as main image and open the segmentation image ```Brain_example/Segmentation.nii``` as segmentation. Can for instance be done in [ITK-SNAP](http://www.itksnap.org/).

For detailed parameter and output description go to the [automatic segmentation program](./automatic_segmentation_program).

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

* The program requires Python 3.7+. You can get the newest version of python by installing Anaconda (https://www.anaconda.com). To check your version of python, type ```python --version```
* Install or update all packages listed in `requirements.txt`. All packages can be installed/updated using `pip`. To install a package, e.g. nibabel: ```pip install nibabel```. To check the version of a package ```pip freeze | grep nibabel```
* Install ANTs, preferably using precompiled binaries, available (for Mac/Linux) at [https://github.com/ANTsX/ANTs/releases/tag/v2.1.0](https://github.com/ANTsX/ANTs/releases/tag/v2.1.0)
* Create ANTSPATH and add it to your path. This is done by editing the config file for the terminal shell. *The config-file is often named ```~/.bashrc``` or ```~/.bashprofile```*. In the config-file add the following lines
    ```sh
    export ANTSPATH=usr/local/bin/ANTs
    export PATH=$PATH:$ANTSPATH
    ```
    Here, ANTs was installed in ```/usr/local/bin/ANTs```.
    
    After adding this to the config-file, restart the terminal and test if the path was added correctly by typing
    ```sh
    which antsRegistration
    ```
    The correct path should then be printed. If permission is denied, the file needs to be made executable - this is done using the command ```chmod +x <...>/antsRegistration```



## Prerequisites  
* A high-resolution, DAPI modality TIFF image of a coronal mouse brain slice. Can have multiple channels.  
* The DAPI template file (.nii or .nii.gz)  
* A segmentation of the DAPI template  (.nii or .nii.gz)



## Usage  
Use runner.py to preprocess and output a segmentation registred to the slice. For detailed parameter and output description go to the [automatic segmentation program](./automatic_segmentation_program).

## Overlay with AllenCCF
To visualize the Allen CCF on top of the DAPI template:

 1. Download the [DAPI template](./data/dapi_template.nii.gz).
 2. Download the current AllenCCF template in the desired resolution from the [download page](https://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/average_template/).
 3. Download the [transformation](./ccf_to_dapi_affine_transformation.txt).
 4. Apply the transformation to the AllenCCF template. Using *average_template_25.nrrd* as  example, this can either be done with:

**ANTs**
```
antsApplyTransforms \
 -i average_template_25.nrrd \
 -d 3 \
 -r dapi_template.nii.gz \
 -o average_template_25_registered_to_dapi.nii.gz \
 -t ccf_to_dapi_affine_transformation.txt
```

**ITK-SNAP**

 1. Open the DAPI template (*dapi_template.nii.gz*) in ITK-SNAP
 2. Add the AllenCCF template (*average_template_25.nrrd*) as a seperate image (File->Add Another Image).
 3. Open Tools->Registration, which will open a panel to the right.
 4. In the bottom right, press the folder icon (*'Load transformation from file'*) and choose the transformation (*ccf_to_dapi_affine_transformation.txt*).
 5. If you want to save the transformed AllenCCF, press the reslice icon (*'Reslice the moving...'*) and choose *ok*. Now you have 3 small images in the top right corner of each plane. Press the bottom one, press the little drop-down in the small image, and choose *'save image'*.