259 citations · 266 across the 4 of their papers we have counts for
7 papers
Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation
Jue Jiang, Harini Veeraraghavan
Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain…
Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT
Hyemin Um, Jue Jiang, Maria Thor +4
We implemented and evaluated a multiple resolution residual network (MRRN) for multiple normal organs-at-risk (OAR) segmentation from computed tomography (CT) images for thoracic r…
Local block-wise self attention for normal organ segmentation
Jue Jiang, Elguindi Sharif, Hyemin Um +2
We developed a new and computationally simple local block-wise self attention based normal structures segmentation approach applied to head and neck computed tomography (CT) images…
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…
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…
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…