YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.
Authors
Affiliations (4)
Affiliations (4)
- ALCOR Lab, Department of Computer, Control and Management Engineering, Faculty of Information Engineering, Informatics and Statistics, Sapienza University of Rome, 00185 Rome, Italy.
- Department of Engineering and Science, Mercatorum University of Rome, Piazza Mattei 10, 00186 Rome, Italy.
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, 00161 Rome, Italy.
- Clinic of General, Special Care, and Geriatric Dentistry, Center for Dental Medicine, University of Zürich, 8032 Zurich, Switzerland.
Abstract
Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for joint tooth segmentation and periapical lesion detection in panoramic radiographs. Our approach employs a joint process: first, a deep learning model identifies and segments individual teeth according to standard dental numbering systems, while a second one detects periapical lesions within the tooth regions obtained from the segmentation outputs in the first stage. The framework incorporates an advanced loss function (Powerful IoU v2) to improve bounding-box regression accuracy and a spatial association mechanism to map detected lesions to specific teeth based on geometric overlap analysis. Our proposed tooth segmentation model achieves an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%, while the periapical lesion detector reaches an mAP@50 of 91.9%. Furthermore, our region-of-interest approach yields a 3.49× computational speedup, averaging 0.1589 s per radiograph when compared to full-image processing. Trained exclusively on open-source datasets, this reproducible framework achieves explicit tooth-to-lesion mapping, providing an efficient and practical tool for periapical lesion screening.