no code implementations • 16 Jul 2021 • Jue Jiang, Andreas Rimner, Joseph O. Deasy, Harini Veeraraghavan
Network design, methods to combine MRI with CT information, distillation learning under informative (MRI to CT), weak (CT to MRI) and equal teacher (MRI to MRI), and ablation tests were performed.
no code implementations • 17 Feb 2021 • Jue Jiang, Sadegh Riyahi Alam, Ishita Chen, Perry Zhang, Andreas Rimner, Joseph O. Deasy, Harini Veeraraghavan
Validation was done on 20 weekly CBCTs from patients not used in training.
1 code implementation • 18 Jul 2020 • Jue Jiang, Yu Chi Hu, Neelam Tyagi, Andreas Rimner, Nancy Lee, Joseph O. Deasy, Sean Berry, Harini Veeraraghavan
Our method achieved an overall average DSC of 0. 87 on T1w and 0. 90 on T2w for the abdominal organs, 0. 82 on T2wFS for the parotid glands, and 0. 77 on T2w MRI for lung tumors.
no code implementations • MIDL 2019 • Hyemin Um, Jue Jiang, Maria Thor, Andreas Rimner, Leo Luo, Joseph O. Deasy, Harini Veeraraghavan
Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections.
no code implementations • 10 Sep 2019 • Jue Jiang, Jason Hu, Neelam Tyagi, Andreas Rimner, Sean L. Berry, Joseph O. Deasy, Harini Veeraraghavan
Our approach, called cross-modality educed deep learning segmentation (CMEDL) combines CT and pseudo MR produced from CT by aligning their features to obtain segmentation on CT.
no code implementations • 31 Jan 2019 • Jue Jiang, Yu-Chi Hu, Neelam Tyagi, Pengpeng Zhang, Andreas Rimner, Joseph O. Deasy, Harini Veeraraghavan
This method produced the highest segmentation accuracy with a DSC of 0. 75 and the lowest Hausdroff distance on the test dataset.