The output of the imageDatastore is a tall cell array, so you'll almost certainly want to use cell2mat to convert this to a tall array.
There's an additional complexity - tall arrays can be "large" only in the first dimension, so it's going to be easiest to perform per-image computations by first using permute on each image so that the dimensions are offset by 1. An example is probably in order.
imds = imageDatastore(fullfile(matlabroot, 'toolbox', 'images', 'imdata', 'AT*.tif'));
t = tall(imds);
At this point, t is an M×1 tall cell array where each cell is a 480×640 uint8 array. If we use cell2mat at this point, we'll end up with a (M*480)×640 uint8 array. By using permute on each cell prior to calling cell2mat, we can end up instead with an M×480×640 uint8 array.
t2 = cellfun(@(im) permute(im, [3, 1, 2]), t, 'UniformOutput', false);
t2 = cell2mat(t2);
gather(size(t2)) % gets [10 480 640]
Now, we can perform mean on each image separately
meanPerImage = mean(mean(t2, 2), 3)
gather(meanPerImage)
Unfortunately, in this case, this turns out to be not the most efficient way to compute this mean. It works better to do:
sumPerImage = sum(sum(t2, 2), 3);
numelPerImage = cellfun(@numel, t);
meanPerImage2 = gather(sumPerImage ./ numelPerImage)
