Li Yanfeng, Ren Yihan, Cheng Zhanyi, Sun Jia, Pan Pan, Chen Houjin
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People's Republic of China.
Phys Med Biol. 2024 May 30;69(11). doi: 10.1088/1361-6560/ad4d53.
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 cm) 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.
在计算机辅助诊断系统中，自动乳腺超声（ABUS）图像中肿瘤区域的准确分割至关重要。然而，肿瘤的内在多样性和成像干扰给ABUS肿瘤分割带来了巨大挑战。在本文中，我们提出了一种结合图融合的全局与局部特征交互模型（GLGM），用于三维ABUS肿瘤分割。在GLGM中，我们构建了一个双分支编码器-解码器，可同时提取局部和全局特征。此外，设计了一个全局与局部特征融合模块，利用最深层次的语义交互促进局部和全局特征之间的信息交换。此外，为提高对小肿瘤的分割性能，设计了一个基于图卷积的浅层特征融合模块。它利用浅层特征增强小肿瘤在局部和全局域中的特征表达。所提出的方法在一个私有ABUS数据集和一个公共ABUS数据集上进行了评估。对于私有ABUS数据集，小肿瘤（体积小于1立方厘米）占整个数据集的50%以上。实验结果表明，所提出的GLGM模型在三维ABUS肿瘤分割方面优于几种当前最先进的分割模型，特别是在分割小肿瘤方面。
