Hey Guys, I mentioned this briefly at SciPy, but I would like to reiterate: a lot of data is 3D images these days, and more and more data is being generated that is multi-channel, 3D+t. Therefore, it would be awesome if scikit-image started making more of an effort to support these. In the best case, the dimension of the underlying array can be abstracted away — see here<https://github.com/jni/ray/blob/develop/ray/morpho.py#L328>for example, the functions juicy_center (which extracts the centre of an array, along all dimensions), surfaces (grabs the "border" arrays along each dimension), hollowed (zeroes-out the centre), and more. Otherwise, writing a 3D function that gracefully degrades to 2D when one of the dimensions is 1 is also possible. In general, the amount of additional effort to make code 3-, 4- or n- dimensional is relatively low when you write the algorithm initially, relative to refactoring a whole bunch of functions later. I'll try to fiddle with whichever code I need, but in the meantime, what do you think about adding a paragraph or a sentence about this issue in the scikit-image contribute <http://scikit-image.org/docs/dev/contribute.html> section, so that people at least have this in mind when they are thinking of writing something new? Thanks, Juan.