用多模态MRI和影像组学提升脑胶质瘤分割与分类精度
ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization
- 基于3D U-Net改进网络，融合多尺度特征与残差跳跃连接
- 在BraTS多个数据集上达到94%以上分割准确率
- 适合医学影像分析、肿瘤智能诊断领域研究者参考
胶质瘤是致死率高、诊断复杂的恶性肿瘤。现有研究存在影像数据变异大、计算资源利用不足、肿瘤分割与分类效率低等问题。为此，本文提出首个基于影像组学增强的融合残差多参数3D网络（ReFRM3D），在3D U-Net基础上引入多尺度特征融合、混合上采样及扩展残差跳跃机制，并设计基于多特征肿瘤标记的分类器，提取分割区域的影像组学特征。实验表明，在BraTS2019、BraTS2020、BraTS2021数据集上，整体肿瘤（WT）、增强肿瘤（ET）、肿瘤核心（TC）的Dice相似系数分别达94.04%、92.68%、93.64%；94.09%、92.91%、93.84%；93.70%、90.36%、92.13%，显著优于现有方法。
原文摘要 · Abstract (English)
Gliomas are among the most aggressive cancers, characterized by high mortality rates and complex diagnostic processes. Existing studies on glioma diagnosis and classification often describe issues such as high variability in imaging data, inadequate optimization of computational resources, and inefficient segmentation and classification of gliomas. To address these challenges, we propose novel techniques utilizing multi-parametric MRI data to enhance tumor segmentation and classification efficiency. Our work introduces the first-ever radiomics-enhanced fused residual multiparametric 3D network (ReFRM3D) for brain tumor characterization, which is based on a 3D U-Net architecture and features multi-scale feature fusion, hybrid upsampling, and an extended residual skip mechanism. Additionally, we propose a multi-feature tumor marker-based classifier that leverages radiomic features extracted from the segmented regions. Experimental results demonstrate significant improvements in segmentation performance across the BraTS2019, BraTS2020, and BraTS2021 datasets, achieving high Dice Similarity Coefficients (DSC) of 94.04%, 92.68%, and 93.64% for whole tumor (WT), enhancing tumor (ET), and tumor core (TC) respectively in BraTS2019; 94.09%, 92.91%, and 93.84% in BraTS2020; and 93.70%, 90.36%, and 92.13% in BraTS2021.
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