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Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth.

Y. Huo, Z. Xu, S. Bao, A. Assad, R. Abramson, and B. Landman. CoRR, (2017)

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G. Daiß

G. Wagner

Univ. -Prof. Dr. Richard Dipper University of Stuttgart

Richard Powers University of Stuttgart

Richard Gozali University of Stuttgart

Other publications of authors with the same name

Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks.Y. Huo, Z. Xu, S. Bao, C. Bermudez, H. Moon, P. Parvathaneni, T. Moyo, M. Savona, A. Assad, R. Abramson and 1 other author(s). CoRR, (2018)Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth.Y. Huo, Z. Xu, S. Bao, A. Assad, R. Abramson, and B. Landman. CoRR, (2017)Fully convolutional neural networks improve abdominal organ segmentation.M. Bobo, S. Bao, Y. Huo, Y. Yao, J. Virostko, A. Plassard, I. Lyu, A. Assad, R. Abramson, M. Hilmes and 1 other author(s). Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 105742V. SPIE, (2018)Efficient abdominal segmentation on clinically acquired CT with SIMPLE context learning.Z. Xu, R. Burke, C. Lee, R. Baucom, B. Poulose, R. Abramson, and B. Landman. Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94130L. SPIE, (2015)Improving splenomegaly segmentation by learning from heterogeneous multi-source labels.Y. Tang, Y. Huo, Y. Xiong, H. Moon, A. Assad, T. Moyo, M. Savona, R. Abramson, and B. Landman. Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 1094908. SPIE, (2019)Shape-constrained multi-atlas segmentation of spleen in CT.Z. Xu, B. Li, S. Panda, A. Asman, K. Merkle, P. Shanahan, R. Abramson, and B. Landman. Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 903446. SPIE, (2014)Multi-atlas segmentation for abdominal organs with Gaussian mixture models.R. Burke, Z. Xu, C. Lee, R. Baucom, B. Poulose, R. Abramson, and B. Landman. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9417 of SPIE Proceedings, page 941707. SPIE, (2015)Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks.Y. Huo, Z. Xu, S. Bao, C. Bermudez, H. Moon, P. Parvathaneni, T. Moyo, M. Savona, A. Assad, R. Abramson and 1 other author(s). IEEE Trans. Med. Imaging, 38 (5): 1185-1196 (2019)SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth.Y. Huo, Z. Xu, H. Moon, S. Bao, A. Assad, T. Moyo, M. Savona, R. Abramson, and B. Landman. CoRR, (2018)Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly.Y. Huo, J. Liu, Z. Xu, R. Harrigan, A. Assad, R. Abramson, and B. Landman. Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101330A. SPIE, (2017)

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Disambiguation of "Abramson, Richard G."

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Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth.

Please choose a person to relate this publication to

G. Daiß

G. Wagner

Univ. -Prof. Dr. Richard Dipper University of Stuttgart

Richard Powers University of Stuttgart

Richard Gozali University of Stuttgart

Other publications of authors with the same name

Disambiguation

PUMA

Disambiguation of "Abramson, Richard G."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth.

Please choose a person to relate this publication to

G. Daiß

G. Wagner

Univ. -Prof. Dr. Richard Dipper University of Stuttgart

Richard Powers University of Stuttgart

Richard Gozali University of Stuttgart

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
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Chicago
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Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth.