Abstract：
Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.
摘要：
目前,深度学习在图像分割领域发展迅速,医学图像分割是该领域的关键应用之一。常规CNN在一般医学图像分割任务中取得了巨大的成功,但是它在特征提取部分存在特征丢失，并且缺乏显式建模远程依赖的能力，很难适应人体器官分割的任务。尽管包含注意力机制的方法在语义分割领域取得了良好的进展,当前的大多数注意力机制仅限于单个样本，虽然人体器官图像的样本数量很大，忽略样本之间的相关性不利于图像分割。为了解决这些问题,本文提出了一种内部和外部双注意分割网络(IEA-Net)，并在该网络中设计了带残差的交错卷积系统(ICSwR)模块和IEAM模块。ICSwR包含交错卷积和跳频连接，用于编码器部分中的特征的初始提取。IEAM模块（内部和外部双注意模块）由LGGW-SA（局部-全局高斯加权自注意）模块和EA模块组成，它们是串联结构。LGGW-SA模块专注于学习单个样本内的局部-全局特征相关性，以实现高效的特征提取。同时,EA模块旨在捕获样品间连接，解决多样本复杂性。此外,跳过连接将被合并到编码器和解码器内的每个IEAM模块中,以减少特征损失。我们在Synapse多器官分割数据集和ACDC心脏分割数据集上测试了我们的方法，实验结果表明，该方法比其他最先进的方法具有更好的性能。
