6 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
Nested-block self-attention for robust radiotherapy planning segmentation
Harini Veeraraghavan, Jue Jiang, Sharif Elguindi +5
Although deep convolutional networks have been widely studied for head and neck (HN) organs at risk (OAR) segmentation, their use for routine clinical treatment planning is limited…
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…
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Sadegh Riyahi, Wookjin Choi, Chia-Ju Liu +8
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation. Currently, local MTV changes in esophageal (s…
Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening
Wookjin Choi, Saad Nadeem, Sadegh Riyahi +3
Spiculations are important predictors of lung cancer malignancy, which are spikes on the surface of the pulmonary nodules. In this study, we proposed an interpretable and parameter…