TY - GEN
T1 - Sparse Convolutional Neural Network for Skull Reconstruction
AU - Kroviakov, Artem
AU - Li, Jianning
AU - Egger, Jan
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Patient-specific implant (PSI) design is a challenging task and requires a specialist, who will spend a significant amount of time using computer aided design tools for implant creation, since patient-specific skull features have to be accounted for. Automating this process could potentially allow intraoperative PSI availability at a relatively low cost. This work proposes to use a 3D Sparse Convolutional Neural Network (SCNN) to reconstruct complete skulls given defective skulls (i.e., skull shape completion) and extract implants by taking the difference between them. With the help of recently published methods for sparse convolutions, it is now possible to avoid the downsampling of the whole skull volume, which is required for conventional dense 3D CNN applications proposed previously. Thus, the SCNN-based approach allows to preserve the original skull geometry. The proposed pipeline includes a supervised SCNN autoencoder network with data preprocessing steps, which further exploit the sparse nature of a skull scan. The best setup in our experiments achieves a Dice Score (DS) of 85.4%, a Border Dice Score of 94.6%, Hausdorff Distance (HD) of 4.91 and 95th percentile HD of 2.64 on the dataset for Task 3 of the AutoImplant 2021 challenge (https://autoimplant2021.grand-challenge.org/ ). The results are comparable with a dense CNN counterpart which has significantly more parameters and requires downsampling and cropping of the skull image on 6GB GPUs. The code is publicly available at https://github.com/akroviakov/SparseSkullCompletion.
AB - Patient-specific implant (PSI) design is a challenging task and requires a specialist, who will spend a significant amount of time using computer aided design tools for implant creation, since patient-specific skull features have to be accounted for. Automating this process could potentially allow intraoperative PSI availability at a relatively low cost. This work proposes to use a 3D Sparse Convolutional Neural Network (SCNN) to reconstruct complete skulls given defective skulls (i.e., skull shape completion) and extract implants by taking the difference between them. With the help of recently published methods for sparse convolutions, it is now possible to avoid the downsampling of the whole skull volume, which is required for conventional dense 3D CNN applications proposed previously. Thus, the SCNN-based approach allows to preserve the original skull geometry. The proposed pipeline includes a supervised SCNN autoencoder network with data preprocessing steps, which further exploit the sparse nature of a skull scan. The best setup in our experiments achieves a Dice Score (DS) of 85.4%, a Border Dice Score of 94.6%, Hausdorff Distance (HD) of 4.91 and 95th percentile HD of 2.64 on the dataset for Task 3 of the AutoImplant 2021 challenge (https://autoimplant2021.grand-challenge.org/ ). The results are comparable with a dense CNN counterpart which has significantly more parameters and requires downsampling and cropping of the skull image on 6GB GPUs. The code is publicly available at https://github.com/akroviakov/SparseSkullCompletion.
KW - Shape completion
KW - Sparse Convolutional Neural Network (SCNN)
UR - http://www.scopus.com/inward/record.url?scp=85121927315&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-92652-6_7
DO - 10.1007/978-3-030-92652-6_7
M3 - Conference paper
AN - SCOPUS:85121927315
SN - 9783030926519
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 80
EP - 94
BT - Towards the Automatization of Cranial Implant Design in Cranioplasty 2 - Second Challenge, AutoImplant MICCAI 2021, Proceedings
A2 - Li, Jianning
A2 - Egger, Jan
A2 - Li, Jianning
A2 - Egger, Jan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention
Y2 - 1 October 2021 through 1 October 2021
ER -