NiftyNet is difficult to set up due to dependency issues (e.g., TensorFlow versions). Packaging as a CONDA package, providing a Docker container (GPU/CPU), and Jupyter Notebook support would make it more accessible to a wider audience.
NiftyNet is amazing, but getting it going on various computers is quite difficult. Especially since it does not use the latest Tensorflow (1.12 vs. 1.13), finding the appropriate CUDA and dependencies can be difficult. Have you considered packaging it as a CONDA package? A Docker container for GPU/CPU would be convenient as well. A Jupyter Notebook would make deployment/inference easy. I would not be that much work, but would make this accessible to a much larger audience. https://www.pugetsystems.com/labs/hpc/How-to-Install-TensorFlow-with-GPU-Support-on-Windows-10-Without-Installing-CUDA-UPDATED-1419/ https://towardsdatascience.com/tensorflow-gpu-installation-made-easy-use-conda-instead-of-pip-52e5249374bc Thanks!