论文标题
双平面X射线图像的3D重建膝盖骨骼的端到端卷积神经网络
End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images
论文作者
论文摘要
我们提出了一种直接从两个双平面X射线图像的膝盖骨头重建的端到端卷积神经网络（CNN）方法。临床上，捕获骨骼的3D模型对于手术计划，植入物配件和术后评估至关重要。与计算机断层扫描（CT）成像相比，X射线成像显着降低了患者对电离辐射的暴露，与磁共振成像（MRI）扫描仪相比，比计算机断层扫描（CT）成像更为普遍和便宜。但是，从这种2D扫描中检索3D模型极具挑战性。与对每个骨骼形状建模的常见方法相反，我们的深网直接从训练图像中学习了骨骼形状的分布。我们使用CT扫描产生的数字重构X光片（DRR）图像，通过监督和无监督的损失训练模型。为了将模型应用于X射线数据，我们使用样式传输来转换X射线和DRR模式。结果，在测试时，我们的解决方案在没有进一步优化的情况下直接从一对双平面X射线图像中输出3D重建，同时保留几何约束。我们的结果表明，我们的深度学习模型非常有效，可以很好地概括并产生高质量的重建。
We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images. Clinically, capturing the 3D models of the bones is crucial for surgical planning, implant fitting, and postoperative evaluation. X-ray imaging significantly reduces the exposure of patients to ionizing radiation compared to Computer Tomography (CT) imaging, and is much more common and inexpensive compared to Magnetic Resonance Imaging (MRI) scanners. However, retrieving 3D models from such 2D scans is extremely challenging. In contrast to the common approach of statistically modeling the shape of each bone, our deep network learns the distribution of the bones' shapes directly from the training images. We train our model with both supervised and unsupervised losses using Digitally Reconstructed Radiograph (DRR) images generated from CT scans. To apply our model to X-Ray data, we use style transfer to transform between X-Ray and DRR modalities. As a result, at test time, without further optimization, our solution directly outputs a 3D reconstruction from a pair of bi-planar X-ray images, while preserving geometric constraints. Our results indicate that our deep learning model is very efficient, generalizes well and produces high quality reconstructions.
