改进U-Net模型，提升肾肿瘤在CT图像中的分割精度
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation
- 多层特征融合+跨通道注意力机制增强编码器特征提取
- 肾肿瘤分割DSC达0.96，优于现有最优模型
- 适合医学影像分割研究者及临床辅助诊断开发者
肾细胞癌（RCC）具有显著异质性，给基于MRI、超声和CT等影像的诊断带来挑战。基于U-Net的深度学习方法正成为肾肿瘤自动分割的有前景方案，但当前方法在准确率上仍需提升以满足临床需求。本文提出一种改进的U-Net模型，用于端到端的CT图像语义分割，以识别肾肿瘤。该模型在编码器块中集成多层特征融合（MFF）与跨通道注意力（CCA），并利用残差连接与增强的跳跃连接引入额外信息。在包含210名患者的KiTS19数据集上评估，肾脏分割DSC为0.97，Jaccard指数为0.95；肾肿瘤分割DSC为0.96，Jaccard指数为0.91。与现有DSC得分对比，本模型表现更优。
原文摘要 · Abstract (English)
Renal tumors, especially renal cell carcinoma (RCC), show significant heterogeneity, posing challenges for diagnosis using radiology images such as MRI, echocardiograms, and CT scans. U-Net based deep learning techniques are emerging as a promising approach for automated medical image segmentation for minimally invasive diagnosis of renal tumors. However, current techniques need further improvements in accuracy to become clinically useful to radiologists. In this study, we present an improved U-Net based model for end-to-end automated semantic segmentation of CT scan images to identify renal tumors. The model uses residual connections across convolution layers, integrates a multi-layer feature fusion (MFF) and cross-channel attention (CCA) within encoder blocks, and incorporates skip connections augmented with additional information derived using MFF and CCA. We evaluated our model on the KiTS19 dataset, which contains data from 210 patients. For kidney segmentation, our model achieves a Dice Similarity Coefficient (DSC) of 0.97 and a Jaccard index (JI) of 0.95. For renal tumor segmentation, our model achieves a DSC of 0.96 and a JI of 0.91. Based on a comparison of available DSC scores, our model outperforms the current leading models.
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