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Head CT collection for patient-specific craniofacial implant (PSI) design

Version 2 2021-03-05, 15:16
Version 1 2020-11-20, 11:29
posted on 2020-11-20, 11:29 authored by Jianning LiJianning Li, Christina GsaxnerChristina Gsaxner, Antonio PepeAntonio Pepe, Ana Morais, Victor Alves, Gord von Campe, Jürgen Wallner, Jan EggerJan Egger
Patient-specific cranial implants are used to repair bone defects in the human skull after trauma or previous surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is both time-consuming and expensive. Recent advances in additive manufacturing (AM) made the in-hospital or in-operation room (in-OR) fabrication of personalized implants feasible. However, the design of implants is still manually performed by external manufacturer. To facilitate an optimized surgical workflow, fast and automatic implant design is highly desirable. Data-driven approaches, such as deep learning, show great potential towards automatic implant design. However, a considerable amount of data is needed to train such algorithms which is, especially in the medical domain, often a bottleneck. Therefore, we present a data set containing CT scans of healthy skulls from 24 patients, in which we injected various artificial cranial defects, resulting in 240 data pairs. In addition, we provide the 240 corresponding implants for these data pairs. With our collection, we also disclose a toolbox for processing our data, providing users wanting to work on automatic cranial implant design a solid base for their research.

Toolbox Link:

Please use the following citation if you use the data in your work:
J. Li, et al. Head CT Collection for Patient-specific Craniofacial Implant (PSI) Design. Figshare, 2020.
J. Li, et al. Synthetic Skull Bone Defects for automatic Patient-specific Craniofacial Implant Design. Sci. Data, 2020.

The datasets can be viewed with StudierFenster:

Please see also our AutoImplant Challenge for cranial implant design:


FWF KLI 678-B31 (enFaced)

COMET K-Project 871132 (CAMed)

TU Graz LEAD Project "Mechanics, Modeling and Simulation of Aortic Dissection"

Overseas Visiting Scholars Program from the Shanghai Jiao Tong University (SJTU) in China