  We propose a novel 3D fully convolutional deep network for automated pancreas
segmentation from both MRI and CT scans. More specifically, the proposed model
consists of a 3D encoder that learns to extract volume features at different
scales; features taken at different points of the encoder hierarchy are then
sent to multiple 3D decoders that individually predict intermediate
segmentation maps. Finally, all segmentation maps are combined to obtain a
unique detailed segmentation mask. We test our model on both CT and MRI imaging
data: the publicly available NIH Pancreas-CT dataset (consisting of 82
contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans).
Experimental results show that our model outperforms existing methods on CT
pancreas segmentation, obtaining an average Dice score of about 88%, and yields
promising segmentation performance on a very challenging MRI data set (average
Dice score is about 77%). Additional control experiments demonstrate that the
achieved performance is due to the combination of our 3D fully-convolutional
deep network and the hierarchical representation decoding, thus substantiating
our architectural design.
