Abstract
| Original language | English |
|---|---|
| Title of host publication | New Knowledge in Information Systems and Technologies |
| Subtitle of host publication | WorldCIST'19 2019 |
| Editors | Á. Rocha, H. Adeli, L. Reis, S. Costanzo |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 151-160 |
| Volume | 1 |
| ISBN (Electronic) | 978-3-030-16187-3 |
| ISBN (Print) | 978-3-030-16186-6 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 2019 World Conference on Information Systems and Technologies - Galicia, Spain Duration: 16 Apr 2019 → 19 Apr 2019 |
Publication series
| Name | Advances in Intelligent Systems and Computing |
|---|---|
| Volume | 932 |
Conference
| Conference | 2019 World Conference on Information Systems and Technologies |
|---|---|
| Abbreviated title | WorldCIST 2019 |
| Country | Spain |
| City | Galicia |
| Period | 16/04/19 → 19/04/19 |
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Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder. / Morais, Ana; Egger, Jan; Alves, Victor.
New Knowledge in Information Systems and Technologies: WorldCIST'19 2019. ed. / Á. Rocha; H. Adeli; L. Reis; S. Costanzo. Vol. 1 Cham : Springer, 2019. p. 151-160 (Advances in Intelligent Systems and Computing; Vol. 932).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder
AU - Morais, Ana
AU - Egger, Jan
AU - Alves, Victor
PY - 2019
Y1 - 2019
N2 - 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 …
AB - 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 …
U2 - 10.1007/978-3-030-16187-3_15
DO - 10.1007/978-3-030-16187-3_15
M3 - Conference contribution
SN - 978-3-030-16186-6
VL - 1
T3 - Advances in Intelligent Systems and Computing
SP - 151
EP - 160
BT - New Knowledge in Information Systems and Technologies
A2 - Rocha, Á.
A2 - Adeli, H.
A2 - Reis, L.
A2 - Costanzo, S.
PB - Springer
CY - Cham
ER -