A straightforward implementation of a U-Net for segmentation. Developed at CVLab@EPFL as part of our work for Task 5.6.3 of the Human Brain Project (SGA1), whose ultimate goal is to develop tools for automated processing of medical images and integrate them into ilastik. For related repositories, please see:
- https://github.com/etrulls/ilastik: ilastik fork with support for our plugin.
- https://github.com/etrulls/unet-service: service to interface ilastik with pre-trained U-Net models.
The implementation allows for 2D or 3D filters. Settings are specified via configuration files: see config/spec.cfg for options and other .cfg files for examples. Datasets are not made public as they are undergoing curation, but they should be in the future (please do inquire). Using your own data should be easy: follow the examples on dataset.py to format it as lists of slices (2D) or stacks (3D). This code is provided as-is, without further support.
Requires pytorch 0.3. For an overcomplete list of dependencies, please check reqs.txt.