论文标题
使用低分辨率3D形状完成和高分辨率2D改进的颅内植入物预测
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
论文作者
论文摘要
设计颅内植入物需要3D了解完整的头骨形状。因此，采用2D方法是最佳选择，因为2D模型缺乏有缺陷和健康的头骨的整体3D视图。此外，在常见的GPU中，以原始图像分辨率加载整个3D头骨形状是不可行的。为了减轻这些问题，我们提出了一个由两个子网组成的完全卷积网络。第一个子网设计旨在完成下采样有缺陷的头骨的形状。第二个子网络为重建形状切片的样本提供了样本。我们将3D和2D网络端对端训练，并具有分层损耗函数。我们提出的解决方案准确地预测了在挑战测试案例中的高分辨率3D植入物，从骰子得分和Hausdorff距离方面。
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 the 3D and 2D networks together end-to-end, 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.
