TY - GEN
T1 - 3D fully convolutional networks for co-segmentation of tumors on PET-CT images
AU - Zhong, Zisha
AU - Kim, Yusung
AU - Zhou, Leixin
AU - Plichta, Kristin
AU - Allen, Bryan
AU - Buatti, John
AU - Wu, Xiaodong
N1 - Funding Information:
This work was supported in part by the National Science Foundation (NSF) under Grant CCF-1733742, and in part by the the National Institutes of Health (NIH) under Grants R01-EB004640 and 1R21CA209874.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the powerful fully convolutional networks (FCN) based semantic segmentation framework (3D-UNet) and the graph cut based co-segmentation model. First, two separate deep UNets are trained on PET and CT, separately, to learn high level discriminative features to generate tumor/non-tumor masks and probability maps for PET and CT images. Then, the two probability maps on PET and CT are further simultaneously employed in a graph cut based co-segmentation model to produce the final tumor segmentation results. Comparative experiments on 32 PET-CT scans of lung cancer patients demonstrate the effectiveness of our method.
AB - Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the powerful fully convolutional networks (FCN) based semantic segmentation framework (3D-UNet) and the graph cut based co-segmentation model. First, two separate deep UNets are trained on PET and CT, separately, to learn high level discriminative features to generate tumor/non-tumor masks and probability maps for PET and CT images. Then, the two probability maps on PET and CT are further simultaneously employed in a graph cut based co-segmentation model to produce the final tumor segmentation results. Comparative experiments on 32 PET-CT scans of lung cancer patients demonstrate the effectiveness of our method.
KW - Co-segmentation
KW - Deep learning
KW - Fully convolutional networks
KW - Image segmentation
KW - Lung tumor segmentation
UR - http://www.scopus.com/inward/record.url?scp=85048090740&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2018.8363561
DO - 10.1109/ISBI.2018.8363561
M3 - Conference contribution
C2 - 31772717
AN - SCOPUS:85048090740
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 228
EP - 231
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -