Abstract
In this study, we proposed two methods for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge. The shape of the implant is predicted based on the inputted defective skull. This task can be accomplished either by directly predicting the implant with the defective skull, or indirectly rebuilding the complete skull and then taking the difference between the defective and complete skulls. In our work, a deep learning model is applied to automatically predict the implant. In order to solve the problem that high resolution images can often not be directly inputted to the deep learning model, two proposed methods of resize and patch-based are examined. On the test set, the proposed resize method achieves an average dice similarity score (DSC) of 0.7350 and a Hausdorff distance (HD) of 7.2425 mm, while the proposed patch-based method achieves an average DSC of 0.8887 and a HD of 5.5339 mm.
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Acknowledgements
This work was supported by CAMed (COMET K-Project 871132), which is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) and the Austrian Federal Ministry for Digital and Economic Affairs (BMDW) and the Styrian Business Promotion Agency (SFG). Furthermore, the Austrian Science Fund (FWF) KLI 678-B31: “enFaced: Virtual and Augmented Reality Training and Navigation Module for 3D-Printed Facial Defect Reconstructions” and the TU Graz LEAD Project “Mechanics, Modeling and Simulation of Aortic Dissection”.
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Jin, Y., Li, J., Egger, J. (2020). High-Resolution Cranial Implant Prediction via Patch-Wise Training. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science(), vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_11
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