Search Results for author:
Found 11 papers, 2 papers with code
Wasserstein Image Local Analysis: Histogram of Orientations, Smoothing and Edge Detection
We approach the directionality problem from a novel perspective by the use of the optimal transport map of a local image patch to a uni-color patch of its mean.
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.
Nested-block self-attention for robust radiotherapy planning segmentation
NBSA's segmentations were less variable than multiple 3D methods, including for small organs with low soft-tissue contrast such as the submandibular glands (surface Dice of 0. 90).
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.
Kernel Wasserstein Distance
The Wasserstein distance is a powerful metric based on the theory of optimal transport.
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.
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation.
Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening
The spiculation quantification measures was then applied to the radiomics framework for pathological malignancy prediction with reproducible semi-automatic segmentation of nodule.
