H-FCBFormer：用于咬合纸咬合接触分割的层次全卷积分支Transformer
H-FCBFormer Hierarchical Fully Convolutional Branch Transformer for Occlusal Contact Segmentation with Articulating Paper
AI总结：
本文提出H-FCBFormer模型，结合Vision Transformer与全卷积网络及层次损失函数，用于咬合纸图像的咬合接触分割，在检测医学真阳性接触方面优于其他机器学习方法和牙医，且识别速度更快。
摘要：
咬合接触是指上颌骨与下颌骨后牙咬合面相接触的位置。咬合接触检测是恢复咀嚼功能丧失的重要工具，也是牙科领域的一项强制性评估，在口腔修复学和修复牙科学中尤为重要。最常用的咬合接触检测方法是咬合纸。然而，该方法可能指示出显著的医学假阳性和医学假阴性接触区域，使得真实咬合指征的识别依赖于临床医生。为解决这一问题，我们提出了一种多类别Vision Transformer与全卷积网络集成的语义分割模型，并采用组合层次损失函数，我们将其命名为层次全卷积分支Transformer（H-FCBFormer）。我们还提出了一种生成医学真阳性语义分割掩码的方法，该方法源自专家标注的咬合纸掩码和金标准掩码。所提出的模型在检测医学真阳性接触方面优于其他评估过的机器学习方法，并且在准确识别物体级咬合接触区域方面表现优于牙医，同时识别所需时间显著更少。代码可在https://github.com/Banksylel/H-FCBFormer获取。
英文摘要：
Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and restorative dentistry. The most common method for occlusal contact detection is articulating paper. However, this method can indicate significant medically false positive and medically false negative contact areas, leaving the identification of true occlusal indications to clinicians. To address this, we propose a multiclass Vision Transformer and Fully Convolutional Network ensemble semantic segmentation model with a combination hierarchical loss function, which we name as Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer). We also propose a method of generating medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of accurately identifying object-wise occlusal contact areas while taking significantly less time to identify them. Code is available at https://github.com/Banksylel/H-FCBFormer.
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。
