Automatic Segmentation Method for Cone-Beam Computed Tomography Image of the Bone Graft Region within Maxillary Sinus Based on the Atrous Spatial Pyramid Convolution Network

Sinus floor elevation with a lateral window approach requires bone graft (BG) to ensure sufficient bone mass, and it is necessary to measure and analyse the BG region for follow-up of postoperative patients. However, the BG region from cone-beam computed tomography (CBCT) images is connected to the margin of the maxillary sinus, and its boundary is blurred. Common segmentation methods are usually performed manually by experienced doctors, and are complicated by challenges such as low efficiency and low precision. In this study, an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution (ASPC) network. The ASPC module was adopted using residual connections to compose multiple atrous convolutions, which could extract more features on multiple scales. Subsequently, a segmentation network of the BG region with multiple ASPC modules was established, which effectively improved the segmentation performance. Although the training data were insufficient, our networks still achieved good auto-segmentation results, with a dice coefficient (Dice) of 87.13%, an Intersection over Union (Iou) of 78.01%, and a sensitivity of 95.02%. Compared with other methods, our method achieved a better segmentation effect, and effectively reduced the misjudgement of segmentation. Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’ work efficiency, which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.