Authors: Lukas Folle,Sulaiman Vesal,Nishant Ravikumar,Andreas Maier
ArXiv: 1903.09097
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Abstract URL: http://arxiv.org/abs/1903.09097v1
Tissue loss in the hippocampi has been heavily correlated with the
progression of Alzheimer's Disease (AD). The shape and structure of the
hippocampus are important factors in terms of early AD diagnosis and prognosis
by clinicians. However, manual segmentation of such subcortical structures in
MR studies is a challenging and subjective task. In this paper, we investigate
variants of the well known 3D U-Net, a type of convolution neural network (CNN)
for semantic segmentation tasks. We propose an alternative form of the 3D
U-Net, which uses dilated convolutions and deep supervision to incorporate
multi-scale information into the model. The proposed method is evaluated on the
task of hippocampus head and body segmentation in an MRI dataset, provided as
part of the MICCAI 2018 segmentation decathlon challenge. The experimental
results show that our approach outperforms other conventional methods in terms
of different segmentation accuracy metrics.