H
Heran Yang
Researcher at Xi'an Jiaotong University
Publications - 11
Citations - 317
Heran Yang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 5, co-authored 8 publications receiving 150 citations. Previous affiliations of Heran Yang include Johns Hopkins University.
Papers
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Book ChapterDOI
Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN
Heran Yang,Heran Yang,Jian Sun,Aaron Carass,Can Zhao,Junghoon Lee,Zongben Xu,Jerry L. Prince +7 more
TL;DR: In this article, a structure-constrained cycleGAN was proposed for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency.
Journal ArticleDOI
Unsupervised MR-to-CT Synthesis Using Structure-Constrained CycleGAN
TL;DR: This paper proposes a structure-constrained cycleGAN for unsupervised MR-to-CT synthesis by defining an extra structure-consistency loss based on the modality independent neighborhood descriptor and utilizes a spectral normalization technique to stabilize the training process and a self-attention module to model the long-range spatial dependencies in the synthetic images.
Posted Content
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN
Heran Yang,Heran Yang,Jian Sun,Aaron Carass,Can Zhao,Junghoon Lee,Zongben Xu,Jerry L. Prince +7 more
TL;DR: A structure-constrained cycleGAN is proposed for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency.
Book ChapterDOI
Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images
TL;DR: Experimental results on Cardiac MR images for left ventricular segmentation demonstrate that the proposed deep fusion net approach is effective both in atlas selection and multi-atlas label fusion, and achieves state of the art in performance.
Journal ArticleDOI
Neural multi-atlas label fusion: Application to cardiac MR images.
TL;DR: The proposed novel multi‐atlas segmentation method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non‐local patch‐based label fusion (NL‐PLF) subnet in a single network.