This report provides an overview of the current state of the art deep
learning architectures and optimisation techniques, and uses the ADNI
hippocampus MRI dataset as an example to compare the effectiveness and
efficiency of different convolutional architectures on the task of patch-based
3-dimensional hippocampal segmentation, which is important in the diagnosis of
Alzheimer's Disease. We found that a slightly unconventional "stacked 2D"
approach provides much better classification performance than simple 2D patches
without requiring significantly more computational power. We also examined the
popular "tri-planar" approach used in some recently published studies, and
found that it provides much better results than the 2D approaches, but also
with a moderate increase in computational power requirement. Finally, we
evaluated a full 3D convolutional architecture, and found that it provides
marginally better results than the tri-planar approach, but at the cost of a
very significant increase in computational power requirement.