Search Results for author:
Found 6 papers, 1 papers with code
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation
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.
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation
Validation was done on 20 weekly CBCTs from patients not used in training.
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
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.
Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT
Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections.
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation
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.
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets
This method produced the highest segmentation accuracy with a DSC of 0. 75 and the lowest Hausdroff distance on the test dataset.
