ScholarWorks@중앙대학교

초록

This study investigates a diagnostic pipeline for panoramic dental X-ray analysis that integrates tooth enumeration and disease detection, and quantitatively compares the diagnostic performance of various combinations of segmentation and detection models applied within this pipeline. In the tooth localization stage, detection (DINO) and segmentation models (SE U-Net, Mask2Former, OneFormer) were jointly utilized to enhance boundary level precision. For disease detection, three versions of YOLO models were adopted to evaluate structural differences in performance. Using the MICCAI Dentex Challenge 2023 dataset, nine combinations of segmentation and detection models were evaluated. OneFormer was the most accurate segmentation model and YOLOv9 the most accurate disease detector when assessed individually. In the integrated pipeline, the combination of OneFormer and YOLOv8 achieved the highest average precision of 0.411, while the combination of SE U-Net and YOLOv9 showed the highest average recall of 0.622. Interestingly, detection models had a greater influence on overall performance, while segmentation models contributed meaningfully once they achieved sufficient localization quality. With the baseline fusion weight, YOLOv8 achieved the highest precision and YOLOv9 achieved the highest recall. Increasing the YOLO weight led to YOLOv9 delivering the best overall performance.

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