An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design

TL;DR

This paper presents an automated, cloud-based system utilizing deep learning for skull defect restoration and cranial implant design, streamlining pre-surgical planning and enabling rapid, patient-specific implant creation for cranioplasty procedures.

Contribution

The authors developed and integrated a fully automatic cranial implant design system into an online platform, enhancing existing workflows with deep learning for shape completion and implant generation.

Findings

Automated skull shape completion using deep learning.

Direct download of implant models in STL format.

Potential integration into clinical practice for faster surgeries.

Abstract

We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfenster.tugraz.at/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically restores the missing part of a skull (i.e., skull shape completion) and generates the desired implant by subtracting the defective skull from the completed skull. The generated implant can be downloaded in the STereoLithography (.stl) format directly via the browser interface of the system. The implant model can then be sent to a 3D printer for in loco implant manufacturing. Furthermore, thanks to the standard format, the user can thereafter load the model into another application for post-processing whenever necessary. Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e.g., cranioplasty). Our system, although currently intended for educational and research use only, can be seen as an application of additive manufacturing for fast, patient-specific implant design.