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Article Abstract
Objective: This study aimed to develop an advanced method for preoperative planning and surgical guidance using open-source artificial intelligence (AI)-assisted rapid 3D color multimodal image fusion (MIF) and augmented reality (AR) in extracerebral tumor surgical procedures.
Methods: In this prospective trial of 130 patients with extracerebral tumors, the authors implemented a novel workflow combining FastSurfer (AI-based brain parcellation), Raidionics-Slicer (deep learning tumor segmentation), and Sina AR projection. Comparative analysis between AI-assisted 3D-color MIF (group A) and manual-3D-monochrome MIF (group B) was conducted, evaluating surgical parameters (operative time, blood loss, resection completeness), clinical outcomes (complications, hospital stay, modified Rankin Scale [mRS] scores), and technical performance metrics (processing time, Dice similarity coefficient [DSC], 95% Hausdorff distance [HD]).
Results: The AI-3D-color MIF system achieved superior technical performance with brain segmentation in 1.21 ± 0.13 minutes (vs 4.51 ± 0.15 minutes for manual segmentation), demonstrating exceptional accuracy (DSC 0.978 ± 0.012 vs 0.932 ± 0.029; 95% HD 1.51 ± 0.23 mm vs 3.52 ± 0.35 mm). Clinically, group A demonstrated significant advantages with shorter operative duration, reduced intraoperative blood loss, higher rate of gross-total resection, lower complication incidence, and better postoperative mRS scores (all p < 0.05).
Conclusions: The integration of open-source AI tools (FastSurfer/Raidionics) with AR visualization creates an efficient 3D-color MIF workflow that enhances anatomical understanding through color-coded functional mapping and vascular relationship visualization. This system significantly improves surgical precision while reducing perioperative risks, representing a cost-effective solution for advanced neurosurgical planning in resource-constrained settings.
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| http://dx.doi.org/10.3171/2025.4.FOCUS24557 | DOI Listing |
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