Deep learning for contour quality assurance for RTOG 0933: In-silico evaluation.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

PMID: 39222847

Abstract

PURPOSE: To validate a CT-based deep learning (DL) hippocampal segmentation model trained on a single-institutional dataset and explore its utility for multi-institutional contour quality assurance (QA).

Authors

Evan M Porter

Department of Medical Physics, Wayne State University, Detroit, MI, United States of America.
Charles Vu

Department of Radiation Oncology, Corewell Health-East, Royal Oak, MI, United States.
Ina M Sala

Department of Medical Physics, Wayne State University, Detroit, MI, United States; Department of Radiation Oncology, Corewell Health-East, Royal Oak, MI, United States.
Thomas Guerrero

Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States.
Zaid A Siddiqui

Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.

Keywords

Brain Neoplasms Computer Simulation Deep Learning Hippocampus Humans Magnetic Resonance Imaging Quality Assurance, Health Care Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39222847)

Deep learning for contour quality assurance for RTOG 0933: In-silico evaluation.

Abstract

Authors

Keywords

External Resources

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