@inproceedings{eb0eaeb0c7fc4752a5ca62674a69d5b9,
title = "Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder",
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 …",
author = "Ana Morais and Jan Egger and Victor Alves",
year = "2019",
doi = "10.1007/978-3-030-16187-3_15",
language = "English",
isbn = "978-3-030-16186-6",
volume = "1",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "151--160",
editor = "{\'A}. Rocha and H. Adeli and L. Reis and S. Costanzo",
booktitle = "New Knowledge in Information Systems and Technologies",
note = "2019 World Conference on Information Systems and Technologies , WorldCIST 2019 ; Conference date: 16-04-2019 Through 19-04-2019",
}