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 ± 3.8 (range [79.8, 94.8]) % in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.
@article{arxiv.1711.06439,
title = {Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks},
author = {Holger Roth and Masahiro Oda and Natsuki Shimizu and Hirohisa Oda and Yuichiro Hayashi and Takayuki Kitasaka and Michitaka Fujiwara and Kazunari Misawa and Kensaku Mori},
journal= {arXiv preprint arXiv:1711.06439},
year = {2018}
}
Comments
Accepted for oral presentation at SPIE Medical Imaging 2018, Houston, TX, USA Updated experiment in Fig. 4