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Presentation + Paper
2 March 2018 Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
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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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Holger Roth, Masahiro Oda, Natsuki Shimizu, Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Michitaka Fujiwara, Kazunari Misawa, and Kensaku Mori "Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740B (2 March 2018);

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