  Hippocampus segmentation plays a key role in diagnosing various brain
disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer,
depression and others. Nowadays, segmentation is still mainly performed
manually by specialists. Segmentation done by experts is considered to be a
gold-standard when evaluating automated methods, buts it is a time consuming
and arduos task, requiring specialized personnel. In recent years, efforts have
been made to achieve reliable automated segmentation. For years the best
performing authomatic methods were multi atlas based with around 80-85% Dice
coefficient and very time consuming, but machine learning methods are recently
rising with promising time and accuracy performance. A method for volumetric
hippocampus segmentation is presented, based on the consensus of tri-planar
U-Net inspired fully convolutional networks (FCNNs), with some modifications,
including residual connections, VGG weight transfers, batch normalization and a
patch extraction technique employing data from neighbor patches. A study on the
impact of our modifications to the classical U-Net architecture was performed.
Our method achieves cutting edge performance in our dataset, with around 96%
volumetric Dice accuracy in our test data. In a public validation dataset,
HARP, we achieve 87.48% DICE. GPU execution time is in the order of seconds per
volume, and source code is publicly available. Also, masks are shown to be
similar to other recent state-of-the-art hippocampus segmentation methods in a
third dataset, without manual annotations.
