Difference between convolution structures
In absolute, it is one CNN wich takes 3 inputs images. You could see it as 3 separate features extractors (CNN) which merge their results while trained together.
The author obtain 3 2D input from a 3D images by keeping 3 2D images; one in each plane.
Each of these images has multiple channel because they slices the input among the respective axis.

It is called a &quot;multichannel&quot; CNN because it uses differente representation (3 2D images) of the input (3D image) and therefore extract 3 times the features in order to merge then. (like looking an elephant from each side and above)