Výsledky vyhledávání - "Alam, Sadegh"

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

Autor: Lee, Donghoon, Alam, Sadegh R, Jiang, Jue, Zhang, Pengpeng, Nadeem, Saad, Hu, Yu-Chi

Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, wh

Externí odkaz: http://arxiv.org/abs/2106.09076

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

Autor: Dahiya, Navdeep, Alam, Sadegh R, Zhang, Pengpeng, Zhang, Si-Yuan, Yezzi, Anthony, Nadeem, Saad

In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality plann

Externí odkaz: http://arxiv.org/abs/2103.05690

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Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation

Autor: Jiang, Jue, Alam, Sadegh Riyahi, Chen, Ishita, Zhang, Perry, Rimner, Andreas, Deasy, Joseph O., Veeraraghavan, Harini

Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto

Externí odkaz: http://arxiv.org/abs/2102.08556

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

Autor: Alam, Sadegh R, Li, Tianfang, Zhang, Pengpeng, Zhang, Si-Yuan, Nadeem, Saad

Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We developed a semantic physics-based data augmentation method for segmenting eso

Externí odkaz: http://arxiv.org/abs/2006.15713

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