361 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of California, Los AngelesUS8 papers
- Stanford UniversityUS6 papers
- Icahn School of Medicine at Mount SinaiUS4 papers
- University of California, San FranciscoUS4 papers
- University of EdinburghGB4 papers
- Cedars-Sinai Smidt Heart InstituteUS3 papers
- Child Health and Development InstituteUS3 papers
- Shahid Beheshti University of Medical SciencesIR3 papers
- The Queen's Medical Research InstituteGB3 papers
- University of WashingtonUS3 papers
- Brigham Young UniversityUS2 papers
- Columbia UniversityUS2 papers
6 papers · 1 filter
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks
Yuhua Chen, Dan Ruan, Jiayu Xiao +6
Segmentation of multiple organs-at-risk (OARs) is essential for radiation therapy treatment planning and other clinical applications. We developed an Automated deep Learning-based…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking
Yuhua Chen, Jaime L. Shaw, Yibin Xie +2
High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical a…
Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer
Jie Fu, Xinran Zhong, Ning Li +6
Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed…
Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy
Jie Fu, Kamal Singhrao, Minsong Cao +7
Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models,…
Disentangled Representation Learning in Cardiac Image Analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +4
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Reson…