Panoramic Dental Segmentation with a Novel Approach Based on MONAI U-Net and Sliding Window Inference
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © 2025 IEEE.
Açıklama
9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
Anahtar Kelimeler
Deep Learning, Monai, Tooth Segmentation, U-Net
Kaynak
9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
WoS Q Değeri
Scopus Q Değeri
N/A
