Hippocampus Segmentation on Epilepsy and Alzheimer’s Disease Studies with Multiple Convolutional Neural Networks

Diedre Carmo, Bruna Silva, Clarissa Yasuda, Letcia Rittner, Roberto Lotufo

Abstract:

Hippocampus segmentation on magnetic resonance imaging (MRI) is of key
importance for the diagnosis, treatment decision and investigation of neuropsy-
chiatric disorders. Automatic segmentation is a very active research field, with
many recent models involving Deep Learning for such task. However, Deep
Learning requires a training phase, which can introduce bias from the specific
domain of the training dataset. Current state-of-the art methods train their
methods on healthy or Alzheimer’s disease patients from public datasets. This
raises the question whether these methods are capable to recognize the Hip-
pocampus on a very different domain.
In this paper we present a state-of-the-art, open source, ready-to-use hip-
pocampus segmentation methodology, using Deep Learning. We analyze this
methodology alongside other recent Deep Learning methods, in two domains:
the public HarP benchmark and an in-house Epilepsy patients dataset. Our in-
ternal dataset differs significantly from Alzheimer’s and Healthy subjects scans.
Some scans are from patients who have undergone hippocampal resection, due
to surgical treatment of Epilepsy. We show that our method surpasses others
from the literature in both the Alzheimer’s and Epilepsy test datasets.