  Pancreas segmentation in computed tomography imaging has been historically
difficult for automated methods because of the large shape and size variations
between patients. In this work, we describe a custom-build 3D fully
convolutional network (FCN) that can process a 3D image including the whole
pancreas and produce an automatic segmentation. We investigate two variations
of the 3D FCN architecture; one with concatenation and one with summation skip
connections to the decoder part of the network. We evaluate our methods on a
dataset from a clinical trial with gastric cancer patients, including 147
contrast enhanced abdominal CT scans acquired in the portal venous phase. Using
the summation architecture, we achieve an average Dice score of 89.7 $\pm$ 3.8
(range [79.8, 94.8]) % in testing, achieving the new state-of-the-art
performance in pancreas segmentation on this dataset.
