释放应用 UNet 架构和迁移学习在全景 X 光片牙齿分割中的潜力

Rime Bouali, Oussama Mahboub, Mohamed Lazaar
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摘要

在全景射线照片中进行准确的牙齿分割是牙医诊断和治疗牙科疾病的有用工具。在全景 X 光片中对单个牙齿进行分割和标记有助于牙医监测龋齿的形成,检测牙周病导致的骨质流失,并确定受损牙齿的位置和方向。它还可以帮助规划和植入牙科植入物,以及在法医牙科中识别刑事案件中的个人或人类遗骸。随着人工智能的发展,许多基于深度学习的方法正在得到开发和改进。虽然卷积神经网络已被广泛应用于医学图像分割,但 UNet 及其高级架构因其卓越的分割能力而脱颖而出。本研究介绍了四种语义分割 UNet(Classic UNet、Attention UNet、UNet3+ 和 Transformer UNet),用于使用新的塔夫茨牙科数据集对全景放射照片中的牙齿进行精确分割。每个模型都是从 ImageNet 训练的 VGG19 和 ResNet50 模型中进行迁移学习的。与其他文献模型相比,这些模型取得了最好的结果,骰子系数(DC)和交集大于联合(IoU)分别为 94.64% 至 96.98% 和 84.27% 至 94.19%。这一结果表明,Unet 及其变体更适合分割全景射线照片,可用于潜在的牙科临床应用。
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Unleashing the potential of applied UNet architectures and transfer learning in teeth segmentation on panoramic radiographs
Accurate tooth segmentation in panoramic radiographs is a useful tool for dentists to diagnose and treat dental diseases. Segmenting and labeling individual teeth in panoramic radiographs helps dentists monitor the formation of caries, detect bone loss due to periodontal disease, and determine the location and orientation of damaged teeth. It can also aid in both the planning and placement of dental implants, as well as in forensic dentistry for the identification of individuals in criminal cases or human remains. With the advancement of artificial intelligence, many deep learning-based methods are being developed and improved. Although convolutional neural networks have been extensively used in medical image segmentation, the UNet and its advanced architectures stand out for their superior segmentation capacities. This study presents four semantic segmentation UNets (Classic UNet, Attention UNet, UNet3+, and Transformer UNet) for accurate tooth segmentation in panoramic radiographs using the new Tufts Dental dataset. Each model was performed using transfer learning from ImageNet-trained VGG19 and ResNet50 models. The models achieved the best results compared to the other literature models with dice coefficients (DC) and intersection over union (IoU) of 94.64% to 96.98% and 84.27% to 94.19%, respectively. This result suggests that Unet and its variants are more suitable for segmenting panoramic radiographs and could be useful for potential dental clinical applications.
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