Tom Vercauteren

I find the variable name `bn_type` confusing if you end up not using BN... Maybe replace it with `featnorm_type` or something along these lines?

Should we take the opportunity of this PR to address #285 at the same time? In the spirit of TF, we could also maybe go for `feature_normalization` as a flag...

Probably best to stick to the recommended TFRecord if possible. Some relevant links: https://www.tensorflow.org/performance/datasets_performance https://github.com/tensorflow/tensorflow/issues/21129 https://stackoverflow.com/questions/48309631/tensorflow-tf-data-dataset-reading-large-hdf5-files

Thanks @ddrobny. On a related note, tensorflow has a GPU implementation of the Felzenszwalb & Huttenlocher Distance transform: - https://www.tensorflow.org/addons/api_docs/python/tfa/image/euclidean_dist_transform - https://github.com/tensorflow/addons/blob/master/tensorflow_addons/custom_ops/image/cc/kernels/euclidean_distance_transform_op_gpu.cu.cc Note sure if it's differentiable but it would...

As mentioned in #4603, @masadcv just released [FastGeodis](https://github.com/masadcv/FastGeodis), a package to compute Geodesic and Euclidean distance maps in PyTorch.

For the record, a good starting point in terms of implementation is the one from @luiscarlosgph https://github.com/luiscarlosgph/grabcut Itself mostly borrowing from that in OpenCV + code from old CUDA samples

A few more components are now available, with some starting to be used in MONAILabel. @masadcv implemented a wrapper for OpenCV's graphcut algorithm here: https://github.com/masadcv/numpymaxflow There is also a wrapper...

The packaging question and c++ implementation of graphcut can be dealt with independently I think. We can already get a JIT-based approach in MONAI and once a more robust packaging...

@masadcv: Unless there is an existing MONAI-related issue on packaging, can you create one?

We can certainly transfer the [torch-extension-builder](https://github.com/charliebudd/torch-extension-builder) repo to sit under the [Project-MONAI](https://github.com/Project-MONAI) organisation if there is appetite for MONAI to support it.