Synthetic skull bone defects for automatic patient-specific craniofacial implant design

• Published in: Scientific data Vol. 8; no. 1; pp. 36 - 8
• Main Authors: Li, Jianning, Gsaxner, Christina, Pepe, Antonio, Morais, Ana, Alves, Victor, von Campe, Gord, Wallner, Jürgen, Egger, Jan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.01.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116/2396; 692/308/575; 692/698; 692/700/1421/1846/2771; Algorithms; Bone implants; Bone surgery; Computed tomography; Computer-Aided Design; Data Descriptor; Deep learning; Design; Humanities and Social Sciences; Humans; Image processing; Imaging, Three-Dimensional; Manufacturing; multidisciplinary; Patients; Prostheses and Implants; Prosthesis Design; Science; Science (multidisciplinary); Skull; Skull - anatomy & histology; Skull - diagnostic imaging; Skull - pathology; Tomography, X-Ray Computed; Transplants & implants; Trauma
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-021-00806-0

Patient-specific craniofacial implants are used to repair skull bone defects after trauma or surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is usually time-consuming and expensive. Recent advances in additive manufacturing made the in-hospital or in-operation-room fabrication of personalized implants feasible. However, the implants are still manufactured by external companies. To facilitate an optimized workflow, fast and automatic implant manufacturing is highly desirable. Data-driven approaches, such as deep learning, show currently 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 CT-imaging data of the craniofacial complex from 24 patients, in which we injected various artificial cranial defects, resulting in 240 data pairs and 240 corresponding implants. Based on this work, automatic implant design and manufacturing processes can be trained. Additionally, the data of this work build a solid base for researchers to work on automatic cranial implant designs. Measurement(s) Image Acquisition Matrix Size • Image Slice Thickness • craniofacial region Technology Type(s) imaging technique • computed tomography Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13265225