用VGG19改进的SegUNet提升儿童牙科影像分割精度
Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs
- 基于VGG19的定制SegUNet架构，专为儿童牙片设计
- 在儿童牙科全景片数据集上达到97.53%准确率
- 适合临床辅助诊断，尤其适用于牙科结构多样的儿童患者
儿童牙科分割对诊断至关重要，但因牙齿结构差异大且儿科X光片数量少而具挑战性。本研究提出一种基于VGG19主干的定制SegUNet模型，首次应用于儿童牙科全景片数据集（Children's Dental Panoramic Radiographs dataset），实现当前最优性能：准确率97.53%，Dice系数92.49%，交并比（IOU）91.46%，创下该数据集新基准。全面评估显示其在精确率、召回率和特异性等指标上均表现稳健。VGG19主干有效增强特征提取能力，显著提升分割精度。模型具备良好泛化性，可适应多种牙科结构，为儿科牙科临床诊断提供可靠高效的自动化解决方案。
原文摘要 · Abstract (English)
Pediatric dental segmentation is critical in dental diagnostics, presenting unique challenges due to variations in dental structures and the lower number of pediatric X-ray images. This study proposes a custom SegUNet model with a VGG19 backbone, designed explicitly for pediatric dental segmentation and applied to the Children's Dental Panoramic Radiographs dataset. The SegUNet architecture with a VGG19 backbone has been employed on this dataset for the first time, achieving state-of-the-art performance. The model reached an accuracy of 97.53%, a dice coefficient of 92.49%, and an intersection over union (IOU) of 91.46%, setting a new benchmark for this dataset. These results demonstrate the effectiveness of the VGG19 backbone in enhancing feature extraction and improving segmentation precision. Comprehensive evaluations across metrics, including precision, recall, and specificity, indicate the robustness of this approach. The model's ability to generalize across diverse dental structures makes it a valuable tool for clinical applications in pediatric dental care. It offers a reliable and efficient solution for automated dental diagnostics.
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