发布日期: 2025-03-20

更新日期: 2025-03-20

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2025-03-20 更新

Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing

Authors:Ryan Banks, Vishal Thengane, María Eugenia Guerrero, Nelly Maria García-Madueño, Yunpeng Li, Hongying Tang, Akhilanand Chaurasia

Calculating percentage bone loss is a critical test for periodontal disease staging but is sometimes imprecise and time consuming when manually calculated. This study evaluates the application of a deep learning keypoint and object detection model, YOLOv8-pose, for the automatic identification of localised periodontal bone loss landmarks, conditions and staging. YOLOv8-pose was fine-tuned on 193 annotated periapical radiographs. We propose a keypoint detection metric, Percentage of Relative Correct Keypoints (PRCK), which normalises the metric to the average tooth size of teeth in the image. We propose a heuristic post-processing module that adjusts certain keypoint predictions to align with the edge of the related tooth, using a supporting instance segmentation model trained on an open source auxiliary dataset. The model can sufficiently detect bone loss keypoints, tooth boxes, and alveolar ridge resorption, but has insufficient performance at detecting detached periodontal ligament and furcation involvement. The model with post-processing demonstrated a PRCK 0.25 of 0.726 and PRCK 0.05 of 0.401 for keypoint detection, mAP 0.5 of 0.715 for tooth object detection, mesial dice score of 0.593 for periodontal staging, and dice score of 0.280 for furcation involvement. Our annotation methodology provides a stage agnostic approach to periodontal disease detection, by ensuring most keypoints are present for each tooth in the image, allowing small imbalanced datasets. Our PRCK metric allows accurate evaluation of keypoints in dental domains. Our post-processing module adjusts predicted keypoints correctly but is dependent on a minimum quality of prediction by the pose detection and segmentation models. Code: https:// anonymous.4open.science/r/Bone-Loss-Keypoint-Detection-Code. Dataset: https://bit.ly/4hJ3aE7.

计算骨丢失百分比是牙周病分期的重要测试，但手动计算时有时不够精确且耗时。本研究评估了深度学习中关键点与对象检测模型YOLOv8-pose在自动识别局部牙周骨丢失地标、条件和分期方面的应用。YOLOv8-pose在193张经注释的根尖射片上进行了微调。我们提出了一种关键点检测指标——相对正确关键点百分比（PRCK），该指标将指标归一化到图像中牙齿的平均大小。我们还提出了一种启发式后处理模块，该模块使用经过开源辅助数据集训练的辅助实例分割模型来调整某些关键点的预测，使其与相关牙齿的边缘对齐。该模型能够充分检测骨丢失关键点、牙齿框和牙槽嵴吸收，但在检测牙周韧带脱落和牙槽骨分叉参与方面表现不足。经过后处理的模型在关键点检测方面表现出PRCK 0.25为0.726和PRCK 0.05为0.401的结果，牙齿对象检测的mAP 0.5为0.715，牙周分期的内侧骰子得分为0.593，分叉参与度得分为0.280。我们的注释方法提供了一种牙周疾病检测的无关阶段方法，通过确保图像中的每颗牙齿都有大部分关键点存在，允许小型不平衡数据集。我们的PRCK指标允许准确评估牙科领域的关键点。我们的后处理模块可以正确调整预测的关键点，但这取决于姿态检测和分割模型的最小预测质量。代码链接：https://anonymous.4open.science/r/Bone-Loss-Keypoint-Detection-Code。数据集链接：https://bit.ly/4hJ3aE7。

论文及项目相关链接

PDF 31 pages, 7 tables, 5 figures, 3 equations, journal paper submitted to Computers in Biology and Medicine

摘要

本研究应用深度学习关键点与对象检测模型YOLOv8-pose，对局部牙周骨丢失地标、条件和分期进行自动识别。提出一种关键点检测指标——相对正确关键点百分比（PRCK），将指标归一化至图像中牙齿的平均大小。还提出了一种启发式后处理模块，调整某些关键点预测以与相关牙齿的边缘对齐，采用在开源辅助数据集上训练的实例分割模型作为支持。该模型能充分检测骨丢失关键点、牙齿框和牙槽嵴吸收，但在检测牙周韧带脱落和根尖分叉参与方面表现不足。经过后处理的模型在关键点检测方面展示了PRCK 0.25为0.726和PRCK 0.05为0.401的表现，牙齿对象检测的mAP 0.5为0.715，根尖分期内侧骰子分数为0.593，根尖分叉参与的骰子分数为0.28。本研究提出的标注方法为牙周疾病检测提供了一种阶段无关的方法，确保了图像中每颗牙齿都有大部分关键点存在，适用于小型不平衡数据集。PRCK指标允许在牙科领域准确评估关键点。后处理模块能正确调整预测的关键点，但依赖于姿态检测和分割模型的最小预测质量。

关键见解