MSMCNet: Differential context drives accurate localization and edge smoothing of lesions for medical image segmentation
MSMCNet: Differential context drives accurate localization and edge smoothing of lesions for medical image segmentation
复制标题
MSMCNet：差分上下文驱动医学图像分割中病灶的精确定位和边缘平滑
DOI：
10.1016/j.compbiomed.2023.107624
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发表时间：
2023
影响因子：
7.7
通讯作者：
Hanguang Xiao
中科院分区：
文献类型：
--
作者：
Ke Peng;Yulin Li;Qingling Xia;Tianqi Liu;Xinyi Shi;Diyou Chen;Li Li;Hui Zhao;Hanguang Xiao
医学图像分割在临床辅助诊断中起着至关重要的作用。基于UNet的网络架构在医学图像分割领域取得了巨大的成功。然而，大多数方法通常采用逐元素添加或通道合并来融合特征，导致特征信息的差异较小和过多冗余。因此，这导致了诸如不准确的病变定位和分割中的模糊边界的问题。为了缓解这些问题，多尺度减法和多关键上下文转换网络（MSMCNet）提出了医学图像分割。通过构建差异化的上下文表示，MSMCNet强调重要信息，并通过准确定位病变和增强边界感知来实现精确的医学图像分割。具体地说，差异化上下文表示的构建是通过建议的多尺度非交叉减法（MSNS）模块和多关键上下文转换模块（MCCM）来完成的。MSNS模块利用MCCM编码的上下文并重新分配特征图像素的值。在广泛使用的公共数据集上进行了广泛的实验，包括ISIC-2018数据集，COVID-19-CT-Seg数据集，Kvasir数据集以及私人构建的创伤性脑损伤数据集。实验结果表明，我们提出的MSMCNet优于国家的最先进的医学图像分割方法在不同的评价指标。
Medical image segmentation plays a crucial role in clinical assistance for diagnosis. The UNet-based network architecture has achieved tremendous success in the field of medical image segmentation. However, most methods commonly employ element-wise addition or channel merging to fuse features, resulting in smaller differentiation of feature information and excessive redundancy. Consequently, this leads to issues such as inaccurate lesion localization and blurred boundaries in segmentation. To alleviate these problems, the Multi-scale Subtraction and Multi-key Context Conversion Networks (MSMCNet) are proposed for medical image segmentation. Through the construction of differentiated contextual representations, MSMCNet emphasizes vital information and achieves precise medical image segmentation by accurately localizing lesions and enhancing boundary perception. Specifically, the construction of differentiated contextual representations is accomplished through the proposed Multi-scale Non-crossover Subtraction (MSNS) module and Multi-key Context Conversion Module (MCCM). The MSNS module utilizes the context of MCCM coding and redistribute the value of feature map pixels. Extensive experiments were conducted on widely used public datasets, including the ISIC-2018 dataset, COVID-19-CT-Seg dataset, Kvasir dataset, as well as a privately constructed traumatic brain injury dataset. The experimental results demonstrated that our proposed MSMCNet outperforms state-of-the-art medical image segmentation methods across different evaluation metrics.