bl_app_dbb_DisSeg

This application computes the Dice similarity coefficient (DSC) between two segmentation volumes (parcellations)

Author

Gabriele Amorosino ([email protected])

Citation

If you use this code for your research please cite:

Gabriele Amorosino, Denis Peruzzo, Daniela Redaelli, Emanuele Olivetti, Filippo Arrigoni, Paolo Avesani,
DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients,
NeuroImage, 2022, 119486, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2022.119486.

Running the Brainlife App

You can run the BrainLife App DBB_DiceScore on the brainlife.io platform via the web user interface (UI) or using the brainlife CLI. With both of these two solutions, the inputs and outputs are stored on the brainlife.io platform, under the specified project, and the computations are performed using the brainlife.io cloud computing resources.

On Brainlife.io via UI

You can see DBB_DiceScore currently registered on Brainlife. Find the App on brainlife.io and click "Execute" tab and specify dataset e.g. "DBB Distorted Brain Benchmark".

On Brainlife.io using CLI

Brainlife CLI could be installed on UNIX/Linux-based system following the instruction reported in https://brainlife.io/docs/cli/install/.

you can run the App with CLI as follow:

bl app run --id   --project <project_id> --input computed:<parc_object_id> --input ground_truth:<parc_object_id> 

the output is stored in the reference project specified with the id <project_id>. You can retrieve the object_id using the command bl data query, e.g to get the id of the segmentation volume files for the subject 0001 :

bl data query --subject 0001 --datatype "neuro/parcellation/volume"  --project <projectid>

If not present yet, you can upload a new file in a project using bl data upload. For example, in the case of the segmentation volume, for the subject 0001 you can run:

bl data upload --project <project_id> --subject 0001 --datatype "neuro/parcellation/volume" --t1 <full_path> --tag "computed"

Running the code locally

You can run the code on your local machine by git cloning this repository. You can choose to run it with dockers, avoiding to install any software except for singularity. Furthermore, you can run the original script using local software installed.

Run the script using the dockers (recommended)

It is possible to run the app locally, using the dockers that embedded all needed software. This is exactly the same way that apps run code on brainlife.io

Inside the cloned directory, create config.json with something like the following content with the fullpaths to your local input files:

{   
    "computed": "./segmentation.nii.gz",
    "ground_truth": "./parc.nii.gz"
}

Launch the app by executing main.

./main

To avoid using the config file, you can input directly the fullpath of the filess using the script main.sh:

main.sh <computed.ext> <ground_truth.ext> [<output.txt>]

Script Dependecies

The App needs singularity to run.

Output

The output of bl_app_dbb_DisSeg is a .txt file, reporting the dice score of each label separated but a space and sort as the labels value. The file is stored in the working directory.

Run the script (local installed softwares)

Clone this repository using git on your local machine to run this script.

Usage

main_local.sh <computed.ext> <ground_truth.ext> [<output.txt>]

Output

The output of bl_app_dbb_DisSeg is a .txt file, reporting the dice score of each label separated but a space and sort as the labels value. The file is stored in the working directory.

Script Dependecies

It is necessary that Python 2.7.x is installed, with the following modules:

• nibabel=2.5.1
• numpy

It is suggested to install python modules using conda.

conda  conda install -c conda-forge nibabel=2.5.1 \
      && conda install numpy