Tong Fei, Nakao Megumi, Wu Shuqiong, Nakamura Mitsuhiro, Matsuda Tetsuya
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1608-1611. doi: 10.1109/EMBC44109.2020.9176655.
Computed tomography (CT) and magnetic resonance imaging (MRI) scanners measure three-dimensional (3D) images of patients. However, only low-dimensional local two-dimensional (2D) images may be obtained during surgery or radiotherapy. Although computer vision techniques have shown that 3D shapes can be estimated from multiple 2D images, shape reconstruction from a single 2D image such as an endoscopic image or an X-ray image remains a challenge. In this study, we propose X-ray2Shape, which permits a deep learning-based 3D organ mesh to be reconstructed from a single 2D projection image. The method learns the mesh deformation from a mean template and deep features computed from the individual projection images. Experiments with organ meshes and digitally reconstructed radiograph (DRR) images of abdominal regions were performed to confirm the estimation performance of the methods.
计算机断层扫描（CT）和磁共振成像（MRI）扫描仪可测量患者的三维（3D）图像。然而，在手术或放疗过程中，可能只能获得低维局部二维（2D）图像。尽管计算机视觉技术已表明可以从多个2D图像估计3D形状，但从单个2D图像（如内窥镜图像或X射线图像）进行形状重建仍然是一个挑战。在本研究中，我们提出了X射线2形状，它允许从单个2D投影图像重建基于深度学习的3D器官网格。该方法从平均模板和从各个投影图像计算出的深度特征中学习网格变形。对腹部区域的器官网格和数字重建射线照片（DRR）图像进行了实验，以确认这些方法的估计性能。
