Automated Segmentation of Dental CBCT Image Using an Improved U-Net Network

In the field of clinical dental medicine, Cone Beam Computed Tomography (CBCT) is a useful tool for the measurement of various dimensions related to the oral cavity, including height and thickness. This provides invaluable guidance and reference for risk assessment in orthodontic treatment, selection of treatment plans and implant treatment. However, segmentation of the teeth region from CBCT images is a daunting task due to complex root morphology and indistinct boundaries between the root and the alveolar bone. Manual annotation of the teeth area is resource-intensive, and deep learning-based segmentation methods are susceptible to noise, reducing their efficiency. To tackle these complexities, a multi-filter attention module is proposed in this paper, which effectively minimizes the noise in CBCT images through utilization of multiple filters and self-attention techniques. Additionally, an Improved U-Net model is proposed, where the original convolution block in the U-Net is replaced with a Double ConvNeXt block to yield better network performance. Experimentally, the proposed Improved U-Net method showed remarkable progress as it achieved a Dice Similarity Coefficient of 86.95% in oral CBCT image segmentation, surpassing existing models and affirming the effectiveness and advancedness of the proposed model and method.