  Over the past decades, state-of-the-art medical image segmentation has
heavily rested on signal processing paradigms, most notably registration-based
label propagation and pair-wise patch comparison, which are generally slow
despite a high segmentation accuracy. In recent years, deep learning has
revolutionalized computer vision with many practices outperforming prior art,
in particular the convolutional neural network (CNN) studies on image
classification. Deep CNN has also started being applied to medical image
segmentation lately, but generally involves long training and demanding memory
requirements, achieving limited success. We propose a patch-based deep learning
framework based on a revisit to the classic neural network model with
substantial modernization, including the use of Rectified Linear Unit (ReLU)
activation, dropout layers, 2.5D tri-planar patch multi-pathway settings. In a
test application to hippocampus segmentation using 100 brain MR images from the
ADNI database, our approach significantly outperformed prior art in terms of
both segmentation accuracy and speed: scoring a median Dice score up to 90.98%
on a near real-time performance (<1s).
