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Adrian Celaya

Showing results (1-10 of 9) with videos related to

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Journal of Applied Clinical Medical Physics|February 11, 2022
An efficient magnetic resonance image data quality screening dashboardEvan D H Gates, Adrian Celaya, Dima Suki, et al.
Head and Neck Tumor Segmentation for Mr-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, Proceedings|June 11, 2025
Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNetAwj Twam, Adrian Celaya, Evan Lim, et al.
Frontiers in Oncology|August 22, 2025
Evaluation of quantum contouring algorithms for treatment planning on MR abdominal imagesRachel Glenn, Tucker Netherton, Adrian Celaya, et al.
IEEE Transactions on Medical Imaging|November 25, 2022
PocketNet: A Smaller Neural Network for Medical Image AnalysisAdrian Celaya, Jonas A Actor, Rajarajesawari Muthusivarajan, et al.
Biomedical Physics & Engineering Express|May 20, 2026
RADIANT: A fully configurable radiotherapy dose prediction frameworkJosiane Laure Pafeng, Adrian Celaya, Skylar Gay, et al.
Medical Physics|April 19, 2024
Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumorsRajarajeswari Muthusivarajan, Adrian Celaya, Joshua P Yung, et al.
Research Square|May 15, 2024
Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease EtiologiesNihil Patel, Mohamed Eltaher, Rachel Glenn, et al.
Scientific Reports|June 9, 2026
Developing a fully automated imaging biomarker for HCC risk assessment via MRI-based tumor segmentation and EPMAwj Twam, Shane A Smith, Mohamed Eltaher, et al.
Scientific Reports|September 9, 2024
Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologiesNihil Patel, Adrian Celaya, Mohamed Eltaher, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Journal of Applied Clinical Medical Physics|February 11, 2022
An efficient magnetic resonance image data quality screening dashboardEvan D H Gates, Adrian Celaya, Dima Suki, et al.
Head and Neck Tumor Segmentation for Mr-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, Proceedings|June 11, 2025
Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNetAwj Twam, Adrian Celaya, Evan Lim, et al.
Frontiers in Oncology|August 22, 2025
Evaluation of quantum contouring algorithms for treatment planning on MR abdominal imagesRachel Glenn, Tucker Netherton, Adrian Celaya, et al.
IEEE Transactions on Medical Imaging|November 25, 2022
PocketNet: A Smaller Neural Network for Medical Image AnalysisAdrian Celaya, Jonas A Actor, Rajarajesawari Muthusivarajan, et al.
Biomedical Physics & Engineering Express|May 20, 2026
RADIANT: A fully configurable radiotherapy dose prediction frameworkJosiane Laure Pafeng, Adrian Celaya, Skylar Gay, et al.
Medical Physics|April 19, 2024
Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumorsRajarajeswari Muthusivarajan, Adrian Celaya, Joshua P Yung, et al.
Research Square|May 15, 2024
Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease EtiologiesNihil Patel, Mohamed Eltaher, Rachel Glenn, et al.
Scientific Reports|June 9, 2026
Developing a fully automated imaging biomarker for HCC risk assessment via MRI-based tumor segmentation and EPMAwj Twam, Shane A Smith, Mohamed Eltaher, et al.
Scientific Reports|September 9, 2024
Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologiesNihil Patel, Adrian Celaya, Mohamed Eltaher, et al.
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