Akademik Veri Yönetim Sistemi
9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025, Malatya, Türkiye, 6 - 07 Eylül 2025, (Tam Metin Bildiri)
Tooth segmentation plays a vital role in dental diagnosis and treatment planning. Accurate delineation of dental structures from imaging modalities such as panoramic radiographs forms the foundation for various clinical applications, including orthodontic assessment, implant planning, and disease diagnosis. However, this task is considered complex due to challenges such as image noise, low contrast, overlapping anatomical structures, and missing teeth. As a result, traditional image processing techniques often fall short, giving rise to the prominence of deep learning-based segmentation methods. In this study, four distinct deep learning architectures were evaluated to enhance the performance of dental segmentation in panoramic radiographs: SegNet, U-Net, MONAI U-Net, and the proposed MONAI U-Net with Sliding Window Inference (SWI). Notably, while SWI is commonly employed during validation and testing stages in the literature, it was integrated into the training phase in this study. This modification enabled the model to process smaller patches instead of the full image, thereby preserving fine-grained details and improving segmentation accuracy. Experimental results demonstrated that the proposed MONAI U-Net + SWI model outperformed the other models in both Intersection over Union (IoU) and Dice coefficient metrics. These findings suggest that the proposed approach offers a reliable solution for clinically sensitive tasks such as dental segmentation.