| 摘 要: 为了提高CT图像中病灶分割的准确性,辅助医生快速诊断和制订治疗方案,文章采用基于UNet网络结构的深度学习算法,提出了一种在跳跃连接中融入通道域和空间域注意力机制的方法。此方法增强了高层次特征对低层次特征的指导,以达到对小目标病灶的关注。同时,为了提高模型性能,提出了CrossEntropyLoss和DiceLoss的混合损失函数。实验结果表明,改进后的UNet模型分割平均准确率达到90.86%,相较于传统的UNet和SegNet模型,分别提升了3.01百分点和2.38百分点,表现出更高的像素准确率及更快的收敛速度。 |
| 关键词: 图像分割 UNet 注意力模块 损失函数 |
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中图分类号: TP399
文献标识码: A
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| Research on Image Segmentation Based on UNet |
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YU Huaxin, HE Liwen
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(School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
1221077026@njupt.edu.cn; helw@njupt.edu.cn
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| Abstract: In order to improve the accuracy of lesion segmentation in CT images to assist doctors in rapid diagnosis and treatment decision-making, this paper employs a deep learning algorithm based on the UNet network structure and proposes a method that incorporates channel and spatial attention mechanisms into the skip connections. This method enhances the guidance of high-level features on low-level features, allowing for better attention to small target lesions. Additionally, to improve model performance, a hybrid loss function combining Cross Entropy Loss and Dice Loss is introduced. Experimental results show that the modified UNet model achieves an average segmentation accuracy of 90.86% , which is 3.01 percentage points and 2.38 percentage points higher than traditional UNet and SegNet, respectively, showing higher pixel accuracy and faster convergence speed. |
| Keywords: image segmentation UNet attention module loss function |
