AneuSI: A software tool to isolate a region of interest in cerebral aneurysms from full geometrical models

Modeling cerebral aneurysms using patient-specific geometries demands significant computational resources, particularly when analyzing large datasets or to retrieve training data for machine learning techniques. Smaller domains representing the aneurysm and nearby vasculature are preferred to reduce computational cost, but manual extraction of these regions introduces limitations in time, repeatability, and user-dependent bias. In this work, we present and study the accuracy of AneuSI, an open-source software tool designed to automatically extract a region of interest around the aneurysm from the arterial tree without requiring user intervention. The tool is specifically optimized for the AneuriskWeb database format and its centerline data structure, due to its extensive case collection and high quality, curated data. While its modular structure enables future adaptation to other datasets, the current implementation and validation focus on this specific format.

AneuSI is developed in C++ and based on the open-source Visualization Toolkit library. It operates as a command-line tool for Linux, supporting automation through bash scripting. Isolation length is defined as the multiple of a clip factor k and the vessel's inner diameter, obtaining results relative to each aneurysm dimensions. Clip quality was evaluated by comparing AneuSI's output against a trained operator's results for 10 patient-specific lateral aneurysm geometries. The tool was further challenged by processing 102 aneurysm cases from 99 patients across 7 different k values.

AneuSI achieved results comparable to manual isolation in a fraction of the time, averaging 2 s per case in a single processor desktop PC, compared to the 10-15 min needed for manual processing. Applied to the AneuriskWeb database, AneuSI demonstrated a 100% success rate, obtaining 714 isolated models through a total of 2592 cuts.

AneuSI provides a fast, robust, and reproducible solution for automatic aneurysm-centered region extraction, reducing variability while drastically improving efficiency. Its modular architecture enables straightforward integration into existing tools or libraries, and its compatibility with batch processing make it suitable for large-scale biomechanical studies and machine learning dataset generation in cerebrovascular modeling. Certain geometric heuristics were prioritized for computational efficiency; robustness on heterogeneous datasets or alternative centerline formats requires further adaptation.