3 papers
eess.IV2020
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…
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
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…