发布日期: 2025-08-02

更新日期: 2025-08-20

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2025-08-02 更新

PerioDet: Large-Scale Panoramic Radiograph Benchmark for Clinical-Oriented Apical Periodontitis Detection

Authors:Xiaocheng Fang, Jieyi Cai, Huanyu Liu, Chengju Zhou, Minhua Lu, Bingzhi Chen

Apical periodontitis is a prevalent oral pathology that presents significant public health challenges. Despite advances in automated diagnostic systems across various medical fields, the development of Computer-Aided Diagnosis (CAD) applications for apical periodontitis is still constrained by the lack of a large-scale, high-quality annotated dataset. To address this issue, we release a large-scale panoramic radiograph benchmark called “PerioXrays”, comprising 3,673 images and 5,662 meticulously annotated instances of apical periodontitis. To the best of our knowledge, this is the first benchmark dataset for automated apical periodontitis diagnosis. This paper further proposes a clinical-oriented apical periodontitis detection (PerioDet) paradigm, which jointly incorporates Background-Denoising Attention (BDA) and IoU-Dynamic Calibration (IDC) mechanisms to address the challenges posed by background noise and small targets in automated detection. Extensive experiments on the PerioXrays dataset demonstrate the superiority of PerioDet in advancing automated apical periodontitis detection. Additionally, a well-designed human-computer collaborative experiment underscores the clinical applicability of our method as an auxiliary diagnostic tool for professional dentists.

根尖周病变是一种常见的口腔病理现象，给公共卫生带来了重大挑战。尽管不同医疗领域的自动诊断系统取得了进展，但根尖周病变的计算机辅助诊断（CAD）应用的发展仍然受到大规模高质量注释数据集的缺乏的限制。为了解决这个问题，我们发布了一个大规模的全景X射线基准测试集“PerioXrays”，其中包括3673张图像和5662个精心标注的根尖周病变实例。据我们所知，这是第一个用于自动根尖周病变诊断的基准数据集。本文进一步提出了面向临床的根尖周病变检测（PerioDet）范式，它结合了背景去噪注意（BDA）和IoU动态校准（IDC）机制，以应对自动检测中的背景噪声和小目标带来的挑战。在PerioXrays数据集上的大量实验表明，PerioDet在推进自动根尖周病变检测方面的优越性。此外，精心设计的人机协作实验强调了我们方法作为专业牙医的辅助诊断工具的临床适用性。

论文及项目相关链接

PDF MICCAI 2025(Early Accept)

Summary

这篇论文针对牙尖周炎这一常见口腔病理进行了介绍，并发布了一个大规模的全景X射线基准测试数据集“PerioXrays”，包含3,673张图像和5,662个精细标注的牙尖周炎实例。此外，论文提出了一种面向临床的牙尖周炎检测（PerioDet）方法，通过结合背景降噪注意力（BDA）和IoU动态校准（IDC）机制，解决了背景噪声和小目标检测的挑战。在PerioXrays数据集上的广泛实验证明了PerioDet在推进自动化牙尖周炎检测方面的优越性。同时，精心设计的人机协作实验也证明了该方法作为专业牙医的辅助诊断工具的临床适用性。

Key Takeaways