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Proceedings Paper

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
Author(s): Holger Roth; Masahiro Oda; Natsuki Shimizu; Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Michitaka Fujiwara; Kazunari Misawa; Kensaku Mori
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Paper Abstract

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.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740B (2 March 2018); doi: 10.1117/12.2293499
Show Author Affiliations
Holger Roth, Nagoya Univ. (Japan)
Masahiro Oda, Nagoya Univ. (Japan)
Natsuki Shimizu, Nagoya Univ. (Japan)
Hirohisa Oda, Nagoya Univ. (Japan)
Yuichiro Hayashi, Nagoya Univ. (Japan)
Takayuki Kitasaka, Aichi Institute of Technology (Japan)
Michitaka Fujiwara, Nagoya Univ. Graduate School of Medicine (Japan)
Kazunari Misawa, Aichi Cancer Ctr. Hospital (Japan)
Kensaku Mori, Nagoya Univ. (Japan)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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