Open-source AI-assisted rapid 3D color multimodal image fusion and preoperative augmented reality planning of extracerebral tumors.

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Neurosurgical focus Pub Date : 2025-07-01 DOI:10.3171/2025.4.FOCUS24557

Xiaolin Hou, Xiaoling Liao, Ruxiang Xu, Fan Fei, Bo Wu

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引用次数: 0

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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开源ai辅助的快速三维彩色多模态图像融合及脑外肿瘤术前增强现实规划。

目的：本研究旨在利用开源人工智能（AI）辅助的快速3D彩色多模态图像融合（MIF）和增强现实（AR）技术，开发一种在脑外肿瘤手术中进行术前规划和手术指导的先进方法。方法：在这项针对130名脑外肿瘤患者的前瞻性试验中，作者实施了一种新的工作流程，结合了FastSurfer（基于人工智能的脑分割）、Raidionics-Slicer（深度学习肿瘤分割）和Sina AR投影。将人工智能辅助的3d -彩色MIF （A组）与手动3d -单色MIF （B组）进行对比分析，评估手术参数（手术时间、出血量、切除完整性）、临床结果（并发症、住院时间、改良Rankin量表[mRS]评分）和技术性能指标（处理时间、Dice相似系数[DSC]、95% Hausdorff距离[HD]）。结果：AI-3D-color MIF系统在1.21±0.13分钟（人工分割为4.51±0.15分钟）内实现了卓越的脑分割技术性能，显示出卓越的准确率(DSC 0.978±0.012 vs 0.932±0.029；95%高清（1.51±0.23 mm vs 3.52±0.35 mm）。临床方面，A组手术时间短、术中出血量少、总切除率高、并发症发生率低、术后mRS评分好，均具有显著优势（p < 0.05）。结论：开源人工智能工具（FastSurfer/Raidionics）与AR可视化的集成创建了一个高效的3d彩色MIF工作流，通过颜色编码的功能映射和血管关系可视化增强了解剖理解。该系统显著提高了手术精度，同时降低了围手术期风险，代表了资源受限环境下高级神经外科计划的成本效益解决方案。

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