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.
設計顱內植入物需要對整個顱骨形狀有一個三維的了解。因此，采用2D方法是次優的，因為2D模型缺乏缺陷和健康顱骨的整體3D視圖。此外，以原始圖像分辨率加載整個3D顱骨形狀在一般的gpu上是不可行的。為了解決這些問題，我們提出了一個由兩個子網絡組成的全卷積網絡。第一個子網絡用於完成下采樣缺陷顱骨的形狀。第二個子網絡對重構后的形狀進行切片采樣。我們以端到端方式串聯訓練3D和2D網絡，並使用分層損失函數。我們提出的解決方案在挑戰測試案例中根據骰子得分和Hausdorff距離准確預測了高分辨率3D種植體。