用混合2D/3D网络自动分割脑胶质瘤，兼顾效率与精度。
Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas
- 结合2D与3D卷积的UNet改进架构，平衡计算效率与空间信息利用。
- 3D分割Dice系数达0.9888，2D分割准确率高达99.77%。
- 适用于临床辅助诊断，可推广至其他医学影像分析任务。
脑肿瘤诊断对现代临床医生而言极具挑战性，其中胶质瘤是中枢神经系统肿瘤的重要类型，具有多样化的亚区域。精准的脑影像分割与定量分析对准确诊断至关重要。由于人工分割耗时且易出错，亟需全自动分割方法。现有技术多基于全卷积神经网络（FCNs），采用2D或3D卷积。但3D卷积计算开销大，2D卷积则难以充分挖掘三维影像的空间信息。为此，本文提出一种融合2D与3D优势的改进模型，基于UNet、Inception和ResNet架构，在BraTS 2018、2019、2020数据集上验证。结果显示，3D分割准确率达98.91%，2D分割准确率为99.77%，对应Dice系数分别为0.8312与0.9888。该模型经微调后可拓展至多种医学影像任务，显著提升临床诊断效率。
原文摘要 · Abstract (English)
Brain tumor diagnosis is a challenging task for clinicians in the modern world. Among the major reasons for cancer-related death is the brain tumor. Gliomas, a category of central nervous system (CNS) tumors, encompass diverse subregions. For accurate diagnosis of brain tumors, precise segmentation of brain images and quantitative analysis are required. A fully automatic approach to glioma segmentation is required because the manual segmentation process is laborious, prone to mistakes, as well as time-consuming. Modern techniques for segmenting gliomas are based on fully convolutional neural networks (FCNs), which can either use two-dimensional (2D) or three-dimensional (3D) convolutions. Nevertheless, 3D convolutions suffer from computational costs and memory demand, while 2D convolutions cannot fully utilize the spatial insights of volumetric clinical imaging data. To obtain an optimal solution, it is vital to balance the computational efficiency of 2D convolutions along with the spatial accuracy of 3D convolutions. This balance can potentially be realized by developing an advanced model to overcome these challenges. The 2D and 3D models implemented here are based on UNET architecture, Inception, and ResNet models. The research work has been implemented on the BraTS 2018, 2019, and 2020 datasets. The best performer of all the models' evaluations metrics for proposed methodologies offer superior potential in terms of the effective segmentation of gliomas. The ResNet model has resulted in 98.91% accuracy for 3D segmentation and 99.77 for 2D segmentations. The dice scores for 2D and 3D segmentations are 0.8312 and 0.9888, respectively. This model can be applied to various other medical applications with fine-tuning, thereby aiding clinicians in brain tumor analysis and improving the diagnosis process effectively.
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