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

TL;DR

This paper introduces a computational framework called PTK that automates puncture trajectory planning for hemorrhagic stroke surgery, improving accuracy and efficiency.

Contribution

The novel contribution is a hybrid AI-driven framework combining deep learning, PCA, and safety modeling for automated puncture path generation.

Findings

PTK achieved a mean surgeon satisfaction score of 4.4/5 for optimized puncture paths.

The average angle difference between automated and manual paths was 16.36°.

PTK demonstrated technical superiority and clinical applicability in five complex cases.

Abstract

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.

Neurosurgery Puncture Toolkit (PTK) generated paths with surgeon‐planned trajectories: red line L1 was the longest axis of hematoma as the first reference line, black point was the centroid of hematoma; blue line L2 was the second reference line as an optimized path. Yellow line was drawn manually as puncture path by the doctor.

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