SkullBreak/SkullFix - Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU138934" target="_blank" >RIV/00216305:26230/21:PU138934 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2352340921001864?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352340921001864?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dib.2021.106902" target="_blank" >10.1016/j.dib.2021.106902</a>

Result language

angličtina

Original language name

SkullBreak/SkullFix - Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Data in Brief (Online)

ISSN

2352-3409

e-ISSN

—

Volume of the periodical

35

Issue of the periodical within the volume

106902

Country of publishing house

US - UNITED STATES

Number of pages

7

Pages from-to

1-7

UT code for WoS article

000647428600003

EID of the result in the Scopus database

2-s2.0-85101856450