Evaluating the impact of different deface algorithms on deep learning segmentation software performance.

Introduction: Data sharing is essential for advancing research in radiation oncology, particularly for training artificial intelligence (AI) models in medical imaging. However, privacy concerns necessitate de-identification of medical images, including defacing operations to remove facial features. This study evaluates the impact of defacing on AI-driven organ segmentation in head-and-neck (HN) computed tomography (CT) images. Methods: Two defacing algorithms, DeIdentifier and Results: The results showed that defacing significantly impacted segmentation of on-face structures (e.g., oral cavity, eyes, lacrimal glands) with reduced DSC (<0.9) and higher HD95 (>2.5 mm), while off-face structures (e.g., brainstem, spinal cord) remained largely unaffected (DSC >0.9, HD95 <2 mm). DeIdentifier better preserved Hounsfield Units (HU) and anatomical consistency than Conclusion: These findings suggest that while defacing alters segmentation accuracy in on-face regions, its overall impact on off-face structures and radiation therapy planning is minimal. Future work should explore domain adaptation techniques to improve model robustness across defaced and non-defaced datasets, ensuring privacy while maintaining segmentation integrity.