基于全景X线片和VGG19骨干的定制SegUNet增强儿童牙科分割
Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs
AI总结：
本研究首次将VGG19骨干的定制SegUNet应用于儿童牙科全景X线片分割，取得97.53%准确率和91.46%IOU，为该数据集建立新基准。
摘要：
儿童牙科分割在牙科诊断中至关重要，由于牙齿结构存在差异且儿童X线图像数量较少，该任务面临独特挑战。本研究提出了一种以VGG19为骨干的定制SegUNet模型，专门用于儿童牙科分割，并将其应用于儿童牙科全景X线片数据集。以VGG19为骨干的SegUNet架构首次在该数据集上得到应用，并取得了最先进的性能。该模型达到了97.53%的准确率、92.49%的Dice系数和91.46%的交并比（IOU），为该数据集设立了新的基准。这些结果表明，VGG19骨干在增强特征提取和提高分割精度方面具有有效性。包括精确率、召回率和特异性在内的多指标综合评估表明该方法具有稳健性。该模型在不同牙齿结构上的泛化能力使其成为儿童牙科护理临床应用的宝贵工具，为自动化牙科诊断提供了可靠且高效的解决方案。
英文摘要：
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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