SciKit-Surgery

SciKit-Surgery provides a modular Python library suite for developing and translating image-guided surgical and augmented reality applications for surgical navigation and intraoperative imaging.

Key Features:

• Modular design: Composed of 13 distinct libraries providing specialized functionality for visualization, augmented reality in surgery, and hardware interfaces compatible with video, tracking, and ultrasound sources.
• Rapid development and translation: Enables assembly of testable clinical applications that can be translated into production without reimplementation.
• Orthogonality and reduced complexity: Maintains strict orthogonality between libraries to minimize dependencies, quantified using lines-of-code and cross-dependency metrics.
• Python and NumPy data types: Uses Python and standard NumPy data types for modular data interchange between libraries.
• Testing and quality control: Employs robust testing and stringent quality-control measures.

Scientific Applications:

• Surgical navigation and image-guided interventions: Supports development and prototyping of navigation systems and image-guided surgical procedures.
• Augmented reality and intraoperative visualization: Enables creation of augmented reality overlays and visualization techniques for intraoperative guidance.
• Hardware integration for intraoperative imaging: Interfaces with video, tracking, and ultrasound sources for integration of imaging and tracking hardware.
• Clinical translation and multicentre trials: Facilitates rapid transition from single-surgeon trials toward multicentre trial deployment.

Methodology:

Comparative analysis with existing platforms and practical example implementations are used to demonstrate library usage and to quantify dependency complexity via lines-of-code and cross-dependency measurements.