arXiv cs.AI· Siri Willems, Ishika Jain, Jens De Winne, Siri Luthman, Hamed Zivari Adab, Roeland Vandebriel, Anna Alperovich, Xiaohan Zhang, Ben Stoffelen, Tom De Schepper, Lien Solie, Steven De Vleeschouwer·· 3 天前AI 评分34
AI 增强高光谱成像用于低级别胶质瘤术中检测的临床研究
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study
AI 导读
一项临床研究用 AI 增强高光谱成像（HSI）在术中检测低级别胶质瘤，3D CNN 结合光谱与空间特征取得最高中位 ROC-AUC 74.5%，高于纯光谱 XGBoost 的 67.9% 和基于 RGB 的 64.0%。研究在匹配组织位置和相同患者级数据划分下对比 HSI 与 RGB 分类，发现肿瘤区域反射率持续偏低，可能与血红蛋白浓度和氧合差异有关。
正文 · 原文
Authors:Siri Willems, Ishika Jain, Jens De Winne, Siri Luthman, Hamed Zivari Adab, Roeland Vandebriel, Anna Alperovich, Xiaohan Zhang, Ben Stoffelen, Tom De Schepper, Lien Solie, Steven De Vleeschouwer
Abstract:Current image-guided neuronavigation techniques, such as intraoperative MRI and ultrasound, are limited by high costs, limited sensitivity and operational complexity. There is a clear need for more efficient systems to enhance intra-operative tumor detection and improve patient outcomes for intrinsic brain tumor resection. Whereas previous intraoperative brain HSI studies have predominantly investigated high-grade gliomas, glioblastomas, or mixed-grade cohorts, this clinical study focuses specifically on low-grade gliomas (LGGs). LGGs strongly resemble normal brain under white light conditions on the operative microscope. This study characterizes spectral differences between healthy and tumor tissue and provides a paired comparison of HSI- and RGB-based classification at matched tissue locations and under identical patient-level data splits. Tumor regions showed consistently lower reflectance, which may be associated with differences in hemoglobin concentration and oxygenation. A range of classification models was evaluated. The 3D CNN, which integrates spectral and spatial features, achieved the highest median ROC-AUC of 74.5%, compared with 67.9% for the spectral-only XGBoost model and 64.0% for the RGB-based model. These results support further investigation of HSI for low-grade glioma classification and quantify its performance relative to conventional RGB imaging in this cohort.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17944 [cs.CV] |
| (or arXiv:2610.17944v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17944 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Siri Willems [view email]
[v1]
Tue, 13 Oct 2026 08:27:54 UTC (8,610 KB)
来源：arXiv cs.AI · arxiv.org