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This document discusses the use of deep learning techniques for automated cranial implant reconstruction, addressing the limitations of traditional methods that require extensive human input and expertise. It presents a standardized volumetric preprocessing workflow and compares five different deep learning architectures, with the Residual Attention 3D U-Net showing the best performance metrics. The findings suggest that attention-enhanced volumetric designs can significantly improve the accuracy and reliab

Author

farhanasharmin3tithi

Language

EN