融合多模态MRI图像提升胶质瘤亚型分类精度
Subclass Classification of Gliomas Using MRI Fusion Technique
- 用UNet分别分割四类病灶，再加权融合2D与3D结果
- 分类准确率99.25%，各项指标均优于现有方法
- 适合医学影像分析、临床辅助诊断研究者参考
胶质瘤是常见的原发性脑肿瘤，具有不同恶性程度和预后。精确分类对治疗方案制定和预后预测至关重要。本研究旨在开发一种算法，融合T1、T2、T1ce和液体抑制反转恢复（FLAIR）序列的MRI图像，以增强对无肿瘤、坏死核心、瘤周水肿和强化肿瘤四类区域的分类效果。实验使用BraTS数据集，对图像进行最大最小归一化预处理。采用UNet架构分别在2D和3D图像上完成坏死核心、瘤周水肿及强化肿瘤的分割。随后，利用加权平均法融合多模态分割结果。结合2D与3D分割输出可同时捕捉切片级细节（如形状、边界、强度分布）与整体空间特征（如范围、纹理、定位）。融合图像作为输入送入预训练ResNet50模型进行胶质瘤亚型分类，模型在80%训练集上训练，20%验证集上评估。所提方法取得99.25%准确率、99.30%精确率、99.10%召回率、99.19%F1分数、84.49%交并比和99.76%特异性，显著优于现有技术。结果凸显了精准分割与分类在辅助诊断中的重要性。
原文摘要 · Abstract (English)
Glioma, the prevalent primary brain tumor, exhibits diverse aggressiveness levels and prognoses. Precise classification of glioma is paramount for treatment planning and predicting prognosis. This study aims to develop an algorithm to fuse the MRI images from T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) sequences to enhance the efficacy of glioma subclass classification as no tumor, necrotic core, peritumoral edema, and enhancing tumor. The MRI images from BraTS datasets were used in this work. The images were pre-processed using max-min normalization to ensure consistency in pixel intensity values across different images. The segmentation of the necrotic core, peritumoral edema, and enhancing tumor was performed on 2D and 3D images separately using UNET architecture. Further, the segmented regions from multimodal MRI images were fused using the weighted averaging technique. Integrating 2D and 3D segmented outputs enhances classification accuracy by capturing detailed features like tumor shape, boundaries, and intensity distribution in slices, while also providing a comprehensive view of spatial extent, shape, texture, and localization within the brain volume. The fused images were used as input to the pre-trained ResNet50 model for glioma subclass classification. The network is trained on 80% and validated on 20% of the data. The proposed method achieved a classification of accuracy of 99.25%, precision of 99.30%, recall of 99.10, F1 score of 99.19%, Intersection Over Union of 84.49%, and specificity of 99.76, which showed a significantly higher performance than existing techniques. These findings emphasize the significance of glioma segmentation and classification in aiding accurate diagnosis.
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