Abstract：
Accurate segmentation of tumor regions in automated breast ultrasound (ABUS) images is of paramount importance in computer-aided diagnosis system. However, the inherent diversity of tumors and the imaging interference pose great challenges to ABUS tumor segmentation. In this paper, we propose a global and local feature interaction model combined with graph fusion (GLGM), for 3D ABUS tumor segmentation. In GLGM, we construct a dual branch encoder-decoder, where both local and global features can be extracted. Besides, a global and local feature fusion module is designed, which employs the deepest semantic interaction to facilitate information exchange between local and global features. Additionally, to improve the segmentation performance for small tumors, a graph convolution-based shallow feature fusion module is designed. It exploits the shallow feature to enhance the feature expression of small tumors in both local and global domains. The proposed method is evaluated on a private ABUS dataset and a public ABUS dataset. For the private ABUS dataset, the small tumors (volume smaller than 1 cm3) account for over 50% of the entire dataset. Experimental results show that the proposed GLGM model outperforms several state-of-the-art segmentation models in 3D ABUS tumor segmentation, particularly in segmenting small tumors.
摘要：
在自动乳腺超声（ABUS）图像中准确分割肿瘤区域在计算机辅助诊断（CAD）系统中至关重要。然而,肿瘤固有的多样性和影像学干扰对ABUS肿瘤分割提出了巨大挑战。在本文中,我们提出了一种结合图融合(GLGM)的全局和局部特征交互模型，用于3DABUS肿瘤分割。在GLGM，我们构造了一个双分支编码器-解码器，可以提取局部和全局特征。此外,设计了一个全局和局部特征融合(GLFF)模块，它采用最深层的语义交互来促进局部和全局特征之间的信息交换。此外,为了提高小肿瘤的分割性能，设计了基于图卷积的浅层特征融合模块(SFFGC)。它利用浅层特征来增强小肿瘤在局部和全局域中的特征表达。在私有ABUS数据集和公共ABUS数据集上对所提出的方法进行评估。对于私有ABUS数据集，小肿瘤（体积小于1厘米3）占整个数据集的50％以上。实验结果表明，所提出的GLGM模型在3DABUS肿瘤分割中优于几种最先进的分割模型，特别是在分割小肿瘤。
