Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement. (January 2023)

• Record Type: Journal Article
• Authors: Hou, Senbao; Zhou, Tao; Liu, Yuncan; Dang, Pei; Lu, Huiling; Shi, Hongbin
• Is Part Of: Computers in biology and medicine. Volume 152(2023)
• Publication Date: 2023-01
• Subjects: Dental panoramic X-ray images -- Medical auxiliary diagnosis -- Deep learning -- Context semantics -- Contrast enhancement; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106296
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background and objective: It is very significant in orthodontics and restorative dentistry that the teeth are segmented from dental panoramic X-ray images. Nevertheless, there are some problems in panoramic X-ray images of teeth, such as blurred interdental boundaries, low contrast between teeth and alveolar bone. Methods: In this paper, The Teeth U-Net model is proposed in this paper to resolve these problems. This paper makes the following contributions: Firstly, a Squeeze-Excitation Module is utilized in the encoder and the decoder. And proposing a dense skip connection between encoder and decoder to reduce the semantic gap. Secondly, due to the irregular shape of the teeth and the low contrast of the dental panoramic X-ray images. A Multi-scale Aggregation attention Block (MAB) in the bottleneck layer is designed to resolve this problem, which can effectively extract teeth shape features and fuse multi-scale features adaptively. Thirdly, in order to capture dental feature information in a larger field of perception, this paper designs a Dilated Hybrid self-Attentive Block (DHAB) at the bottleneck layer. This module effectively suppresses the task-irrelevant background region information without increasing the network parameters. Finally, the effectiveness of the algorithm is validated using a clinical dental panoramic X-ray image datasets. Results: The results of the three comparison experiments are shown that Accuracy, Precision, Recall, Dice, Volumetric Overlap Error and Relative Volume Difference for dental panoramic X-ray teeth segmentation are 98.53%, 95.62%, 94.51%, 94.28%, 88.92% and 95.97% by the proposed model respectively. Conclusion: The proposed modules complement each other in processing every detail of the dental panoramic X-ray images, which can effectively improve the efficiency of preoperative preparation and postoperative evaluation, and promote the application of dental panoramic X-ray in medical image segmentation. There are more accuracy about Teeth U-Net than others model in dental panoramic X-ray teeth segmentation. That is very important to clinical doctors to cure in orthodontics and restorative dentistry. Graphical abstract: Teeth U-Net segmentation mode. Image 1 Highlights: Proposing a Dense Skip connection Modoul(DSM) between encoder and decoder. Designing a Multiscale Aggregation attention Block(MAB) in the bottleneck layer. This paper designs a Dilated Hybrid self-Attentive Block(DHAB). Acc, Pre, Recall, Dice, Voe and Rvd: 98.53%, 95.62%, 94.51%, 94.28%, 88.92% and 95.97%.