Jin Qiangguo, Meng Zhaopeng, Sun Changming, Cui Hui, Su Ran
School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
CSIRO Data61, Sydney, NSW, Australia.
Front Bioeng Biotechnol. 2020 Dec 23;8:605132. doi: 10.3389/fbioe.2020.605132. eCollection 2020.
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.
由于肝脏和肿瘤形状的异质性和扩散性，从CT体积数据中自动提取肝脏和肿瘤是一项具有挑战性的任务。最近，二维深度卷积神经网络因利用大量标记数据集来学习分层特征而在医学图像分割任务中变得流行。然而，很少有研究探讨用于肝脏肿瘤分割的三维网络。在本文中，我们提出了一种三维混合残差注意力感知分割方法，即RA-UNet，以精确提取肝脏区域并从肝脏中分割肿瘤。所提出的网络具有类似于U-Net的基本架构，它通过将低级特征图与高级特征图相结合来提取上下文信息。集成了注意力残差模块，以便注意力感知特征能够自适应变化。这是第一项将注意力残差机制用于从三维医学体积图像中分割肿瘤的工作。我们在公开的MICCAI 2017肝脏肿瘤分割数据集上评估了我们的框架，并在3DIRCADb数据集上测试了其泛化能力。实验表明，我们的架构取得了具有竞争力的结果。
