There has been some discussion about having requirements on new contributions being compatible with ND images. This has been discussed in a few PRs on several occasions and I think it warrants a discussion on the mailing list (though I'm the first to admit, I get too many emails and seldom check mailing lists). Feel free to copy paste this as an issue on GitHub. Personally, I think that the ND requirement is too big to impose on contributions and on reviewers. Often, people just need 2D operations. I think what is important is to keep the API forward looking for ND. We can do things like accept tuples for parameters, instead of "x_center", "y_center", or "r_center", "c_center". We can also just throw errors when people pass in higher dimensions images saying that it isn't implemented and contributions are welcome. The second aspect is that ND is actually very computationally expensive. In many cases, people might have an information dense "2D image" and slight variations on other dimensions. This means that using a full blown 2D algorithm is just in efficient on higher dimensions (3D or Time, or color). Optimizing for the different use cases is just tricky, and won't be done correctly if contributors are asked to include something they have no immediate need for. Finally, ND is just weird. Have a listen to this crazy video: https://youtu.be/mceaM2_zQd8