Introduction: Accurate segmentation of the key mandibular region in the oral panoramic X-ray image is crucial for the diagnosis of the mandibular region and the planning of implant surgery. Because the oral panoramic X-ray image contains many important anatomical information for implant treatment evaluation. However, the fuzzy boundary between each region in the image makes the segmentation task very challenging. In data-driven segmentation methods, corresponding datasets are often required. Due to the limited oral data set at present, there is a bottleneck in clinical application.

Materials and methods: In this paper, we build a panoramic X-ray image dataset for the mandibular region. The dataset has a total of 711 images. The dataset is divided into 8 categories based on the number of teeth and treatment conditions. The annotations include mandible, normal teeth, treated teeth and implants. In terms of network segmentation. According to the local and global characteristics of the dataset, we designed a CBTrans partition network by paralleling the convolution block and the Swin-transform block of the bottleneck structure.

Results: The experimental results show that our proposed network achieves excellent performance on the mandibular region segmentation dataset and the common retina dataset DRIVE.

Conclusion: CBTrans can better extract features locally and globally by combining CNN of the bottleneck structure and Swin Transformer in parallel. CBTrans demonstrates performance advantages over other similar hybrid architecture models.

Keywords: Image segmentation; Oral dataset; Panoramic X-ray image.

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