SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth-Reference-Cited by-同舟云学术

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

Published:2019-04 Issue:4 Volume:38 Page:1016-1025
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Huo Yuankai^ORCID,Xu Zhoubing^ORCID,Moon Hyeonsoo,Bao Shunxing,Assad Albert,Moyo Tamara K.,Savona Michael R.^ORCID,Abramson Richard G.^ORCID,Landman Bennett A.^ORCID

Funder

National Institute on Aging

National Institutes of Health

Vanderbilt University

ViSE/VICTR

National Center for Research Resources

National Center for Advancing Translational Sciences

Vanderbilt IDEAS

ACCRE, Vanderbilt University, through Big Data TIPs Grant

Vanderbilt–Incyte Research Alliance Grant

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

https://ieeexplore.ieee.org/ielaam/42/8679969/8494797-aam.pdf

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