Versatile distance transforms (Euclidean and Geodesic)

Is your feature request related to a problem? Please describe.
Several segmentation pipelines, and in particular interactive segementation ones, rely on some form of distance transform (e.g. Euclidean, Chamfer or Geodesic Distance Transform).

At the moment there is no off-the-shelf pytorch function to compute these.

Describe the solution you'd like
A MONAI-based implementation of distance transforms (ideally differentiable) would be fantastic. It woul accelerate uptake of such methods.

Describe alternatives you've considered

• Relying on scipy functions such as distance_transform_edt and distance_transform_cdt
• Rely on external code such as GeodisTK

Additional context
Example use cases of distance transforms in segmentaiton:

• https://github.com/Project-MONAI/MONAI/pull/1329
• https://github.com/HiLab-git/UGIR
• https://arxiv.org/abs/1710.04043
• https://arxiv.org/abs/1707.00652

The issue names no repository files, tests, or entry points. Start by reviewing the linked MONAI pull request and the scipy distance_transform_edt and distance_transform_cdt alternatives, then clarify the supported Euclidean and geodesic behavior and differentiability requirements. Done means a MONAI-based implementation covers the agreed distance transforms with appropriate validation.