Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy

no code implementations • 16 Jun 2021 • Donghoon Lee, Sadegh R Alam, Jue Jiang, Pengpeng Zhang, Saad Nadeem, Yu-Chi Hu

Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment.

Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation

1 code implementation • 9 Mar 2021 • Navdeep Dahiya, Sadegh R Alam, Pengpeng Zhang, Si-Yuan Zhang, Anthony Yezzi, Saad Nadeem

Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures.

Data Augmentation Translation

Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation

no code implementations • 28 Jun 2020 • Sadegh R Alam, Tianfang Li, Pengpeng Zhang, Si-Yuan Zhang, Saad Nadeem

Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis.

Data Augmentation Domain Adaptation

Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets

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.

Data Augmentation Segmentation +1