Sorry, it's been a busy couple of days! You are generally correct in your reservations of 2D+t vs. true 3D, and it comes back to the fact that we directly use naked NumPy arrays which carry no metadata. The discussion in PR #532 <https://github.com/scikit-image/scikit-image/pull/532> is relevant, for anyone that isn't seeing both. Handling these cases requires additional information, inferred from files or direct from the user in some fashion. As I noted in the PR discussion, this can be accomplished by wrapping the array into a container which also carries metadata, or via a kwarg indicating the behavior of the final channel. My vote is for the latter. On Sunday, April 28, 2013 10:05:40 PM UTC-5, Ankit Agrawal wrote:

On Mon, Apr 29, 2013 at 7:49 AM, Juan Nunez-Iglesias <jni....@gmail.com<javascript:>

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In Marianne's case, there is a 3D volumetric image *in addition to* a time axis.

Furthermore, if the time resolution in t is sufficient, many nD algorithms can be used, along t as well (with suitable parameters e.g. sigma for gaussian gradient magnitude). For an example, see:

Andres, B., Kroeger, T., Briggman, K. L., Denk, W., Korogod, N., Knott, G., Koethe, U., and Hamprecht, F. A. (2012). Globally optimal closed-surface segmentation for connectomics. ECCV, 778–791.

where they use a 3D segmentation method to do tracking in 2D+t video.

@Juan, this was an interesting read. I can feel why the 3D volumetric algorithm fits 2D x t (video), because the task involved is segmentation based tracking. However, I am still not fully convinced(would like to know more such examples if any) and feel that most nD algorithms would work differently on 2D x t and 3D. Thanks.

Regards, Ankit Agrawal, Communication and Signal Processing, IIT Bombay.