Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement

Autor:	Bayat, Amirhossein, Shit, Suprosanna, Kilian, Adrian, Liechtenstein, Jürgen T., Kirschke, Jan S., Menze, Bjoern H.
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.10769 Zobrazit plný text záznamu View this record from Arxiv