Abstract
Computer-aided Design (CAD) software enables the design of patient-specific cranial implants, but it often requires of a lot of manual user-interactions. This paper proposes a Deep Learning (DL) approach towards the automated CAD of cranial implants, allowing the design process to be less user-dependent and even less time-consuming. The problem of reconstructing a cranial defect, which is essentially filling in a region in a skull, was posed as a 3D shape completion task and, to solve it, a Volumetric Convolutional Denoising Autoencoder was implemented using the open-source DL framework PyTorch. The autoencoder was trained on 3D skull models obtained by processing an open-access dataset of Magnetic Resonance Imaging brain scans. The 3D skull models were represented as binary voxel occupancy grids and experiments were carried out for different voxel resolutions. For each experiment, the autoencoder was evaluated in terms of quantitative and qualitative 3D shape completion performance. The obtained results showed that the implemented Deep Neural Network is able to perform shape completion on 3D models of defected skulls, allowing for an efficient and automatic reconstruction of cranial defects.
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Acknowledgements
This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013, 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), the Austrian Science Fund (FWF) KLI 678-B31 and the Erasmus+ Programme. We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board that was used in this research.
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Morais, A., Egger, J., Alves, V. (2019). Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 932. Springer, Cham. https://doi.org/10.1007/978-3-030-16187-3_15
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