School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China.
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China.
Comput Med Imaging Graph. 2024 Jun;114:102370. doi: 10.1016/j.compmedimag.2024.102370. Epub 2024 Mar 16.
Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.
超声图像分割是一项具有挑战性的任务，因为病变类型复杂、边界模糊、图像对比度低，同时还存在噪声和伪影。为了解决这些问题，我们提出了一种用于超声图像自动分割的端到端多尺度特征提取和融合网络（MEF-UNet）。具体来说，我们首先设计了一个选择性特征提取编码器，包括细节提取阶段和结构提取阶段，以精确捕捉病变的边缘细节和整体形状特征。为了增强上下文信息的表示能力，我们在跳过连接部分设计了一个上下文信息存储模块，负责整合来自相邻两层特征图的信息。此外，我们在解码器部分设计了一个多尺度特征融合模块，用于合并不同尺度的特征图。实验结果表明，我们的 MEF-UNet 可以在定量分析和视觉效果方面显著提高分割结果。
