adwaykanhere/Flower-Medicalsegmentation

Flower for 3D medical image segmentation

This repository is maintained to develop and test the Flower framework for 3D medical image segmentation using the data from the Medical Segmentation Decathalon challenge.

This code is inspired by the official Flower FedBN tutorial

Environment Setup

# Create a conda environment
conda create -n um2ii-flower python=3.10 -y

# Activate the environment
conda activate um2ii-flower # or source activate um2ii-flower

# Install requirements
pip install -r requirements.txt

Before you start using the scripts, please ensure that you have the Pancreas and Spleen data accessible locally and in the right format as detailed in our framework SegViz. The direct commands to download and extract the dataset are:

# let's create first a dataset directory
mkdir dataset
cd dataset

# Download Spleen dataset (~1.5GB)
wget https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar
# now extract it
tar -xvf Task09_Spleen.tar

# Download Pancreas dataset (~11.5 GB)
wget https://msd-for-monai.s3-us-west-2.amazonaws.com/Task07_Pancreas.tar
# now extract it
tar -xvf Task07_Pancreas.tar

The Pancreas dataset has normal and tumor labels, so please use the script below to convert the dataset into single-label (organ) only.

import nibabel as nib
data_dir_pan = 'path_to_dir_containing_images'
for image_path in sorted(glob.glob(os.path.join(data_dir_pan, "labelsTr", "*.nii.gz"))):
    image_file = nib.load(image_path)
    image_file_array = nib.load(image_path).get_fdata()
    image_file_array[image_file_array > 1 ] = 1
    image_file_final = nib.Nifti1Image(image_file_array, image_file.affine)

Run the experiment (Centralized)

python3 msd.py --spleen-path <path/to/spleen/dataset> --pancreas-path <path/to/spleen/dataset>

Run the experiment (Federated)

You will need to run the following scripts in separate terminal windows.

# Start the Flower server using 
python3 server.py

# Start the Flower Liver client using
# Specify where dataset is located and where to save local models
python3 client_spleen.py  --spleen-path=dataset/Task09_Spleen --save-path output-spleen

# Start the Flower Pancreas client using
# Specify where dataset is located and where to save local models
python3 client_pan.py --pancreas-path=dataset/Task07_Pancreas --save-path output-pancreas

When client save a model, they will follwow the directory structure: save-path/date/time/<model>

Run the experiment (Federated with Flower Next)

Flower Next integrates a collection of new featuers that will be gradually incorporated in the usual flwr package. But you can start takinga advantage of them by running your experiments making use of ClientApp and ServerApp. You can find a guide on how to upgrade to Flower Next style in the Flower Documentation. For the purpose of this project:

You'll need to run first the SuperLink, to which you can pass certificates if you whish. Check the documentation for that. The lines below show how to do this w/o certificates.

# start the superlink
flower-superlink --insecure

Next, you'll need your SuperNode (i.e. nodes containing the data that will eventually do training) to the SuperLink. Repeat the below for as many nodes as you have, each pointing to their local data.

# launches supernode wich will execute the the `ClientApp` in `client_spleen.py`
flower-client-app client_spleen:app --insecure

# launch supernode pointing to a `ClientApp` making use of the pancreas data
flower-client-app client_pan:app --insecure

With the above done, you'll see nothing seems to happen. The SuperNodes periodically ping the SuperLink for messages that the ServerApp (which we haven't launched yet) is sending them. Without further due, let's launch the ServerApp to start the federation:

flower-server-app server:app --insecure

You'll notice once the N rounds finish, the SuperLink and SuperNode remain idle. You can launch another ServerApp to start a new experiment (yes, without having to restart the SuperNode or SuperLink)