1.摘自：Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
Designing of a cranial implant needs a 3D understanding of the complete skull shape. Thus, taking a 2D approach is sub-optimal, since a 2D model lacks a holistic 3D view of both the defective and healthy skulls. Further, loading the whole 3D skull shapes at its original image resolution is not feasible in commonly available GPUs. To mitigate these issues, we propose a fully convolutional network composed of two subnetworks. The first subnetwork is designed to complete the shape of the downsampled defective skull. The second subnetwork upsamples the reconstructed shape slice-wise. We train both the 3D and 2D networks in tandem in an end-to-end fashion, with a hierarchical loss function. Our proposed solution accurately predicts a high-resolution 3D implant in the challenge test case in terms of dice-score and the Hausdorff distance.
设计颅内植入物需要对整个颅骨形状有一个三维的了解。因此，采用2D方法是次优的，因为2D模型缺乏缺陷和健康颅骨的整体3D视图。此外，以原始图像分辨率加载整个3D颅骨形状在一般的gpu上是不可行的。为了解决这些问题，我们提出了一个由两个子网络组成的全卷积网络。第一个子网络用于完成下采样缺陷颅骨的形状。第二个子网络对重构后的形状进行切片采样。我们以端到端方式串联训练3D和2D网络，并使用分层损失函数。我们提出的解决方案在挑战测试案例中根据骰子得分和Hausdorff距离准确预测了高分辨率3D种植体。
