Deep Anatomical Federated Network is a program for the segmentation of medical images. It relies on a server to provide deep learning models to aid the segmentation, and incremental learning is used to improve the performance. See https://www.dafne.network/ for documentation and user information.
Please install the Visual Studio Redistributable Package under windows: https://aka.ms/vs/16/release/vc_redist.x64.exe Then, run the provided installer
Install the Dafne App from the downloaded .dmg file as usual. Make sure to download the archive appropriate for your architecture (x86 or arm).
The Linux distribution is a self-contained executable file. Simply download it, make it executable, and run it.
Dafne can also be installed with pip
pip install dafne
If you are writing a scientific paper, and you used Dafne for your data evaluation, please cite the following paper:
Santini F, Wasserthal J, Agosti A, et al. Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation. 2023 doi: 10.48550/arXiv.2302.06352.
Run:
python dafne.py <path_to_dicom_img>
The input of the apply function is:
dict({
'image': np.array (2D image)
'resolution': sequence with two elements (image resolution in mm)
'split_laterality': True/False (indicates whether the ROIs should be split in L/R if applicable)
'classification': str - The classification tag of the image (optional, to identify model variants)
})
The output of the classifier is a string. The output of the segmenters is:
dict({
roi_name_1: np.array (2D mask),
roi_name_2: ...
})
The input of the incremental learn functions are:
training data: dict({
'resolution': sequence (see above)
'classification': str (see above)
'image_list': list([
- np.array (2D image)
- np.array (2D image)
- ...
])
})
training outputs: list([
- dict({
roi_name_1: np.array (2D mask)
roi_name_2: ...
})
- dict...
Every entry in the training outputs list corresponds to an entry in the image_list inside the training data.
So len(training_data['image_list']) == len(training_outputs).
Input/Output is based on DOSMA - GPLv3 license
This software includes the Segment Anything Model (SAM) - Apache 2.0 license
Other packages required for this project are listed in requirements.txt
