口腔全景片颏孔分割：一种深度学习方法
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach
AI总结：
本文针对口腔全景片中的颏孔分割任务，构建1000张内部数据集并比较多种深度学习模型，发现Classical UNet表现最佳。
摘要：
准确识别和检测颏孔在牙科学中至关重要，会影响阻生牙拔除、囊肿手术和种植体植入等操作。准确识别这一解剖结构有助于处理术后问题并改善患者预后。此外，本研究旨在加快牙科诊疗流程，提升牙科领域的患者护理水平和医疗效率。本研究采用深度学习方法，从口腔全景放射影像中准确检测并分割颏孔。在模型训练期间使用了圆形和方形两种掩膜类型。研究采用多种分割模型识别和分割颏孔，并使用不同指标评估其有效性。本研究创建了一个包含1000张口腔全景放射影像的内部数据集。实验表明，Classical UNet模型在测试数据上表现异常出色，Dice系数达到0.79，交并比（IoU）达到0.67。此外，ResUNet++和UNet Attention模型表现出具有竞争力的性能，Dice分数分别为0.675和0.676，IoU值分别为0.683和0.671。研究还考察了具有不同骨干架构的迁移学习模型，发现LinkNet取得了最佳结果。总之，本研究强调了经典Unet模型在口腔全景放射影像中准确识别和勾勒颏孔方面的有效性。尽管该任务十分重要，但与分割脑肿瘤或皮肤癌等复杂医学数据集相比，其难度相对较低，因为后者具有多样的尺寸和形状。本研究对优化牙科实践也具有价值，可使从业者和患者受益。
英文摘要：
Precise identification and detection of the Mental Foramen are crucial in dentistry, impacting procedures such as impacted tooth removal, cyst surgeries, and implants. Accurately identifying this anatomical feature facilitates post-surgery issues and improves patient outcomes. Moreover, this study aims to accelerate dental procedures, elevating patient care and healthcare efficiency in dentistry. This research used Deep Learning methods to accurately detect and segment the Mental Foramen from panoramic radiograph images. Two mask types, circular and square, were used during model training. Multiple segmentation models were employed to identify and segment the Mental Foramen, and their effectiveness was evaluated using diverse metrics. An in-house dataset comprising 1000 panoramic radiographs was created for this study. Our experiments demonstrated that the Classical UNet model performed exceptionally well on the test data, achieving a Dice Coefficient of 0.79 and an Intersection over Union (IoU) of 0.67. Moreover, ResUNet++ and UNet Attention models showed competitive performance, with Dice scores of 0.675 and 0.676, and IoU values of 0.683 and 0.671, respectively. We also investigated transfer learning models with varied backbone architectures, finding LinkNet to produce the best outcomes. In conclusion, our research highlights the efficacy of the classical Unet model in accurately identifying and outlining the Mental Foramen in panoramic radiographs. While vital, this task is comparatively simpler than segmenting complex medical datasets such as brain tumours or skin cancer, given their diverse sizes and shapes. This research also holds value in optimizing dental practice, benefiting practitioners and patients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
