Research on Spinal Canal Generation Method based on Vertebral Foramina Inpainting of Spinal CT Images by using BEGAN

Abstract

Abstract Spinal surgery is of high risk due to the possibility of neurologic damage, which may cause life-threatening sequelae. Although the emerging robotic-assisted spinal surgery provides better accuracy compared with traditional surgery, the construction of boundary constraints around the spinal canal for safety in surgery is still required. The establishment of a three-dimensional (3D) model of the spinal canal during preoperative preparation can facilitate the generation of surgical boundary constraints. This article presents a novel framework for spinal canal generation based on spinal CT image inpainting by using the boundary equilibrium generative adversarial network (BEGAN). First, U-net is used to simplify the image features and then ResNet50 is applied to classify the vertebral foramen features and mark the area to be restored. Finally, BEGAN generates the target features to complete the vertebral foramina inpainting for the generation of the spinal canal. The experimental results show that the average accuracies (Mean Intersection over Union) of the vertebral foramina and spine inpainting are 0.9396 and 0.9332, respectively, and the accuracy of image inpainting decreases with increase in the inpainting area. The proposed method can accurately generate the vertebral contours and complete the 3D reconstruction of the spinal canal.