TY - GEN
T1 - An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design
AU - Li, Jianning
AU - Pepe, Antonio
AU - Schwarz-Gsaxner, Christina
AU - Egger, Jan
PY - 2021
Y1 - 2021
N2 - We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfenster.tugraz.at/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically restores the missing part of a skull (i.e., skull shape completion) and generates the desired implant by subtracting the defective skull from the completed skull. The generated implant can be downloaded in the STereoLithography (.stl) format directly via the browser interface of the system. The implant model can then be sent to a 3D printer for in loco implant manufacturing. Furthermore, thanks to the standard format, the user can thereafter load the model into another application for post-processing whenever necessary. Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e.g., cranioplasty). Our system, although currently intended for educational and research use only, can be seen as an application of additive manufacturing for fast, patient-specific implant design.
AB - We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfenster.tugraz.at/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically restores the missing part of a skull (i.e., skull shape completion) and generates the desired implant by subtracting the defective skull from the completed skull. The generated implant can be downloaded in the STereoLithography (.stl) format directly via the browser interface of the system. The implant model can then be sent to a 3D printer for in loco implant manufacturing. Furthermore, thanks to the standard format, the user can thereafter load the model into another application for post-processing whenever necessary. Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e.g., cranioplasty). Our system, although currently intended for educational and research use only, can be seen as an application of additive manufacturing for fast, patient-specific implant design.
KW - 3D printing
KW - Additive manufacturing
KW - Cranial implant design
KW - Cranioplasty
KW - Deep-learning
KW - Skull reconstruction
KW - Studierfenster
UR - https://arxiv.org/abs/2006.00980
UR - http://www.scopus.com/inward/record.url?scp=85101855290&partnerID=8YFLogxK
U2 - 10.1117/12.2580719
DO - 10.1117/12.2580719
M3 - Conference paper
VL - 11598
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Linte, Cristian A.
A2 - Siewerdsen, Jeffrey H.
T2 - SPIE Medical Imaging Conference 2021
Y2 - 14 February 2021 through 18 February 2021
ER -