# Should I design a neural net architecture for 3D data, or serial 2D stacks?

I am attempting building a neural net to segment pixels in volumetric data: I have microscopy images in 3D (about 500x500x500px each) which contain signal from nuclei.

In the data form it means I have a 3D matrix of dimension 500x500x500, which can be approximated by a noise space filled by many 3D gaussians of sigma $\approxeq$ 6–7. The training set has about 90000 segmented nucleis.

My goal is to build a neural net capable of segmenting the pixels where such nuclei are present against the noise and background.

As a raw idea, I am going for an encoder-decoder style neural net.

My intuition tells me there is information to be gained from feeding into the net a 3D cube—as the object itself is a 3D object. But another approach is to train on 2D slices and feed each slice one at a time.

The second solution is a much easier one, with a lot of documentation on 2D pictures pixel segmentation.

Will I loose a lot by doing a 2D stack VS a 3D cube?

Is it even doable to do a 3D cube?

If you think a 3D architecture would be better, could you recommend some papers / documentation?