新模型GCSER-UNet提升脑肿瘤分割精度，助力精准诊疗。
A Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images
- 融合空间与通道注意力，捕捉复杂上下文信息
- 在TCGA LGG数据集达94%骰子分数，超越当前最佳
- 适合医学影像分析与临床辅助诊断研究者
脑癌严重性要求精准的脑肿瘤分割，以支持有效诊断。人工识别成本高、耗时长且易出错，亟需自动化方法。本文提出全局上下文感知的挤压激励残差UNet（GCSER-UNet），融合空间与通道注意力机制，增强模型对复杂空间依赖和上下文信息的捕捉能力。该模型能高效提取多模态MRI图像中的肿瘤区域，在基准数据集上表现优异：在TCGA LGG数据集上达到94%的骰子分数，超过现有最佳水平（91.8%）；在BraTS 2020数据集中，集成方案对整个肿瘤（W）、肿瘤核心（T）、增强肿瘤（E）的骰子分数分别为95%、92%、90%，分别优于当前最佳（94%、93%、88%）。结果表明GCSER-UNet在精准脑肿瘤分割方面具有显著优势，可辅助神经科医生进行癌症管理与治疗规划。
原文摘要 · Abstract (English)
Brain cancer's severity necessitates precise brain tumor segmentation, which is crucial for effective brain tumor diagnosis. Manual identification, burdened by high costs, labor, and error risks, highlights the need for automated methods. In this study, we introduce the Global Context-aware Squeeze and Excite Residual UNet (GCSER-UNet), which facilitates a fusion of spatial and channel-wise attention and thus enhances the model's capacity to capture intricate spatial dependencies and contextual information. GCSER-UNet efficiently extracts tumor segments from multimodal MRI slices, delivering exceptional performance. Evaluations on benchmark databases exhibit its superiority, achieving a notable 94 percent dice score on the TCGA LGG dataset, surpassing the state-of-the-art dice score of 91.8 percent. In the BraTS 2020 dataset, the proposed GCSER-UNet ensemble approach yielded dice scores of 95 percent, 92 percent, and 90 percent for the tumor regions - Whole Tumor (W), Tumor Core (T), and Enhancing Tumor (E), respectively. The current state-of-the-art dice scores were 94 percent, 93 percent, and 88 percent. These compelling outcomes highlight the efficacy of GCSER-UNet in precise brain tumor segmentation and thus can aid neurologists in effective brain cancer management and treatment planning.
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