Publication Details
SkullBreak/SkullFix - Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks
LI, J.
PEPE, A.
GSAXNER, C.
Egger Jan, prof. Dr.
Španěl Michal, doc. Ing., Ph.D. (DCGM)
and others
cranial implant design, cranioplasty, deep learning, volumetric shape learning,
skull, autoimplant
The article introduces two complementary datasets intended for the development of
data-driven solutions for cranial implant design, which remains to be
a time-consuming and laborious task in current clinical routine of cranioplasty.
The two datasets, referred to as the SkullBreak and SkullFix in this article, are
both adapted from a public head CT collection CQ500
(http://headctstudy.qure.ai/dataset) with CC BY-NC-SA 4.0 license. The SkullBreak
contains 114 and 20 complete skulls, each accompanied by five defective skulls
and the corresponding cranial implants, for training and evaluation respectively.
The SkullFix contains 100 triplets (complete skull, defective skull and the
implant) for training and 110 triplets for evaluation. The SkullFix dataset was
first used in the MICCAI 2020 AutoImplant Challenge
(https://autoimplant.grand-challenge.org/) and the ground truth, i.e., the
complete skulls and the implants in the evaluation set are held private by the
organizers. The two datasets are not overlapping and differ regarding data
selection and synthetic defect creation and each serves as a complement to the
other. Besides cranial implant design, the datasets can be used for the
evaluation of volumetric shape learning algorithms, such as volumetric shape
completion. This article gives a description of the two datasets in detail.
@article{BUT168547,
author="KODYM, O. and LI, J. and PEPE, A. and GSAXNER, C. and EGGER, J. and ŠPANĚL, M.",
title="SkullBreak/SkullFix - Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks",
journal="Data in Brief (Online)",
year="2021",
volume="35",
number="106902",
pages="1--7",
doi="10.1016/j.dib.2021.106902",
issn="2352-3409",
url="https://www.sciencedirect.com/science/article/pii/S2352340921001864?via%3Dihub"
}