Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.
翻译:6D 对象基于X射线图像的深度学习模型作出的估计往往使用使用广泛的 CAD 模型和模拟数据进行培训的定制结构。最近的RGB 方法选择使用小型数据集解决估算问题,使其对医疗数据极少能得到的X射线域更具吸引力。我们改进了现有基于RGB的基于RGB的模型(SingleShotPose),以估计灰度X射线图像的标记立方体的6D构成。由于现代X射线系统在程序期间不断调整获取参数,因此,通过建立仅接受真实X射线数据培训的通用解决方案和为X射线获取地理测量调整的通用解决方案。模型回归2D控制点和通过使用透视-点(PnP)的2D/3D通信计算其构成问题,从而能够在所有支持光谱基X射线的X射线域域域域中使用单一的经过培训的模型。由于现代X射线系统在程序期间不断调整从灰度X射线X射线图像中的标记立方体构成参数,因此这种配置估计网络必须考虑这些参数,同时考虑可应用的精确度和可比较的参数,同时选择的精确度为精确度为三度为标准,在标准前方位值为: 的精确度的精确度为: 的精确度为: