6 citations · 6 across the 1 of their papers we have counts for
3 papers
eess.IV2019
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation
Jue Jiang, Jason Hu, Neelam Tyagi +4
Lung tumors, especially those located close to or surrounded by soft tissues like the mediastinum, are difficult to segment due to the low soft tissue contrast on computed tomograp…
cs.CV2019★ 6 cited
Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning
Peter Klages, Ilyes Benslimane, Sadegh Riyahi +5
A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic C…
cs.CV2019
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets
Jue Jiang, Yu-Chi Hu, Neelam Tyagi +4
Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments trai…