开源人工智能辅助的脑外肿瘤快速三维彩色多模态图像融合及术前增强现实规划

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

作者信息

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

出版信息

Neurosurg Focus. 2025 Jul 1;59(1):E12. doi: 10.3171/2025.4.FOCUS24557.

DOI:10.3171/2025.4.FOCUS24557

PMID:40591963

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.

摘要

目的

本研究旨在开发一种先进方法，用于脑外肿瘤手术中使用开源人工智能（AI）辅助的快速三维彩色多模态图像融合（MIF）和增强现实（AR）进行术前规划和手术引导。

方法

在这项针对130例脑外肿瘤患者的前瞻性试验中，作者实施了一种新颖的工作流程，将FastSurfer（基于AI的脑图谱分割）、Raidionics-Slicer（深度学习肿瘤分割）和新浪AR投影相结合。对AI辅助的三维彩色MIF（A组）和手动三维单色MIF（B组）进行了对比分析，评估手术参数（手术时间、失血量、切除完整性）、临床结果（并发症、住院时间、改良Rankin量表[mRS]评分）以及技术性能指标（处理时间、骰子相似系数[DSC]、95%豪斯多夫距离[HD]）。

结果

AI三维彩色MIF系统在脑分割方面实现了卓越的技术性能，用时1.21±0.13分钟（手动分割为4.51±0.15分钟），显示出极高的准确性（DSC为0.978±0.012，而手动分割为0.932±0.029；95%HD为1.51±0.23毫米，而手动分割为3.52±0.35毫米）。临床上，A组在手术持续时间更短、术中失血量减少、全切除率更高、并发症发生率更低以及术后mRS评分更好等方面表现出显著优势（所有p<0.05）。