Publication Details
Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge
ELLIS, D.
Kodym Oldřich, Ing., Ph.D.
Herout Adam, prof. Ing., Ph.D. (DCGM)
Španěl Michal, doc. Ing., Ph.D. (DCGM)
EGGER, J.
and others
AutoImplant II, Cranial implant design, Sparse convolutional neural networks,
Deep learning, Shape completion, Cranioplasty, Craniectomy
Cranial implants are commonly used for surgical repair of craniectomy-induced
skull defects. These implants are usually generated offline and may require days
to weeks to be available. An automated implant design process combined with
onsite manufacturing facilities can guarantee immediate implant availability and
avoid secondary intervention. To address this need, the AutoImplant II challenge
was organized in conjunction with MICCAI 2021, catering for the unmet clinical
and computational requirements of automatic cranial implant design. The first
edition of AutoImplant (AutoImplant I, 2020) demonstrated the general
capabilities and effectiveness of data-driven approaches, including deep
learning, for a skull shape completion task on synthetic defects. The second
AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding
real clinical craniectomy cases as well as additional synthetic imaging data. The
AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull
images with synthetic defects to evaluate the ability of submitted approaches to
generate implants that recreate the original skull shape. Track 3 consisted of
the data from the first challenge (i.e., 100 cases for training, and 110 for
evaluation), and Track 1 provided 570 training and 100 validation cases aimed at
evaluating skull shape completion algorithms at diverse defect patterns. Track 2
also made progress over the first challenge by providing 11 clinically defective
skulls and evaluating the submitted implant designs on these clinical cases. The
submitted designs were evaluated quantitatively against imaging data from
post-craniectomy as well as by an experienced neurosurgeon. Submissions to these
challenge tasks made substantial progress in addressing issues such as
generalizability, computational efficiency, data augmentation, and implant
refinement. This paper serves as a comprehensive summary and comparison of the
submissions to the AutoImplant II challenge. Codes and models are available at
https://github.com/Jianningli/Autoimplant_II.
@article{BUT187557,
author="LI, J. and ELLIS, D. and KODYM, O. and HEROUT, A. and ŠPANĚL, M. and EGGER, J.",
title="Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge",
journal="MEDICAL IMAGE ANALYSIS",
year="2023",
volume="88",
number="102865",
pages="1--15",
doi="10.1016/j.media.2023.102865",
issn="1361-8423",
url="https://www.sciencedirect.com/science/article/abs/pii/S1361841523001251"
}