Liu Liangliang, Wang Ying, Chang Jing, Zhang Pei, Liang Gongbo, Zhang Hui
College of Information and Management Science, Henan Agricultural University, Zhengzhou, China.
Department of Computer Science, Eastern Kentucky University, Richmond, KY, United States.
Front Neuroinform. 2022 May 5;16:859973. doi: 10.3389/fninf.2022.859973. eCollection 2022.
The encoder-decoder-based deep convolutional neural networks (CNNs) have made great improvements in medical image segmentation tasks. However, due to the inherent locality of convolution, CNNs generally are demonstrated to have limitations in obtaining features across layers and long-range features from the medical image. In this study, we develop a local-long range hybrid features network (LLRHNet), which inherits the merits of the iterative aggregation mechanism and the transformer technology, as a medical image segmentation model. LLRHNet adopts encoder-decoder architecture as the backbone which iteratively aggregates the projection and up-sampling to fuse local low-high resolution features across isolated layers. The transformer adopts the multi-head self-attention mechanism to extract long-range features from the tokenized image patches and fuses these features with the local-range features extracted by down-sampling operation in the backbone network. These hybrid features are used to assist the cascaded up-sampling operations to local the position of the target tissues. LLRHNet is evaluated on two multiple lesions medical image data sets, including a public liver-related segmentation data set (3DIRCADb) and an in-house stroke and white matter hyperintensity (SWMH) segmentation data set. Experimental results denote that LLRHNet achieves state-of-the-art performance on both data sets.
基于编码器-解码器的深度卷积神经网络（CNN）在医学图像分割任务中取得了巨大进展。然而，由于卷积固有的局部性，CNN通常在跨层获取特征和从医学图像中获取长距离特征方面存在局限性。在本研究中，我们开发了一种局部-长距离混合特征网络（LLRHNet），它继承了迭代聚合机制和Transformer技术的优点，作为一种医学图像分割模型。LLRHNet采用编码器-解码器架构作为主干，通过迭代聚合投影和上采样来融合孤立层之间的局部低-高分辨率特征。Transformer采用多头自注意力机制从token化的图像块中提取长距离特征，并将这些特征与主干网络中通过下采样操作提取的局部范围特征相融合。这些混合特征用于辅助级联上采样操作以定位目标组织的位置。LLRHNet在两个多病变医学图像数据集上进行了评估，包括一个公共的肝脏相关分割数据集（3DIRCADb）和一个内部的中风和白质高信号（SWMH）分割数据集。实验结果表明，LLRHNet在这两个数据集上均取得了领先的性能。
