A Computational Framework for Automated Puncture Trajectory Planning in Hemorrhagic Stroke Surgery

The treatment surgery for hemorrhagic stroke typically involves a puncture drainage procedure to remove the hematoma. However, the puncture targets for puncture and the puncture trajectory significantly influence the therapeutic outcome. This study proposes a computational framework integrating artificial intelligence (AI)-driven segmentation, principal component analysis (PCA), and empirical optimization to automate puncture path generation.

A software platform named Puncture Trajectory ToolKits (PTK) was developed using C++/Python with ITK/VTK libraries. Key innovations include hybrid segmentation that combines ResNet-50 deep learning and adaptive thresholding for robust hematoma detection. PCA-based longest axis extraction was enhanced by Laplacian mesh smoothing. Skull quadrant theory and safety corridor modeling were used to avoid critical structures. Five complex clinical cases were used to validate the framework's performance.

The framework demonstrated high accuracy in puncture trajectory planning, with the optimized L2 path achieving a mean surgeon satisfaction score of 4.4/5 (Likert scale) compared to manual methods. The average angle difference between automatically generated and manually designed paths was 16.36°. These results highlight PTK's potential to enhance the efficiency and safety of robotic-assisted neurosurgery.

PTK establishes a systematic pipeline for trajectory planning assistance, demonstrating technical superiority over conventional methods. The high acceptance rate among surgeons and improved planning efficiency underscore its clinical applicability. Future integration with robotic systems and validation through clinical trials are warranted.