Computer-aided design (CAD) Cranial implant design Craniotomy Machine learning Patient-specific implants (PSI) Skull deep learning

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Dec 2021
Historique:
received: 04 10 2021
accepted: 25 10 2021
entrez: 24 11 2021
pubmed: 25 11 2021
medline: 25 11 2021
Statut: epublish

Résumé

In this article, we present a skull database containing 500 healthy skulls segmented from high-resolution head computed-tomography (CT) scans and 29 defective skulls segmented from craniotomy head CTs. Each healthy skull contains the complete anatomical structures of human skulls, including the cranial bones, facial bones and other subtle structures. For each craniotomy skull, a part of the cranial bone is missing, leaving a defect on the skull. The defects have various sizes, shapes and positions, depending on the specific pathological conditions of each patient. Along with each craniotomy skull, a cranial implant, which is designed manually by an expert and can fit with the defect, is provided. Considering the large volume of the healthy skull collection, the dataset can be used to study the geometry/shape variabilities of human skulls and create a robust statistical model of the shape of human skulls, which can be used for various tasks such as cranial implant design. The craniotomy collection can serve as an evaluation set for automatic cranial implant design algorithms.

Identifiants

pubmed: 34815988
doi: 10.1016/j.dib.2021.107524
pii: S2352-3409(21)00800-3
pmc: PMC8591340
doi:

Types de publication

Journal Article

Langues

eng

Pagination

107524

Subventions

Organisme : Austrian Science Fund FWF
ID : KLI 678
Pays : Austria

Informations de copyright

© 2021 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Jianning Li (J)

Graz University of Technology (TU Graz), Graz, Styria, Austria.
Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria.
Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Marcell Krall (M)

Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Florian Trummer (F)

Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Afaque Rafique Memon (AR)

Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Antonio Pepe (A)

Graz University of Technology (TU Graz), Graz, Styria, Austria.
Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria.

Christina Gsaxner (C)

Graz University of Technology (TU Graz), Graz, Styria, Austria.
Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria.
Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Yuan Jin (Y)

Graz University of Technology (TU Graz), Graz, Styria, Austria.
Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria.
Research Center for Connected Healthcare Big Data, ZhejiangLab, Hangzhou, Zhejiang, China.

Xiaojun Chen (X)

Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Hannes Deutschmann (H)

Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Ulrike Zefferer (U)

Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Ute Schäfer (U)

Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Gord von Campe (GV)

Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Jan Egger (J)

Graz University of Technology (TU Graz), Graz, Styria, Austria.
Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria.
Medical University of Graz (MedUni Graz), Graz, Styria, Austria.

Classifications MeSH