Di Shuanhu, Zhao Yu-Qian, Liao Miao, Zhang Fan, Li Xiong
IEEE J Biomed Health Inform. 2023 Mar;27(3):1163-1172. doi: 10.1109/JBHI.2022.3181974. Epub 2023 Mar 7.
Liver tumor segmentation plays an essential role in diagnosis and treatment of hepatocellular carcinoma or metastasis. However, accurate and automatic tumor segmentation remains a challenging task, owing to vague boundaries and large variations in shapes, sizes, and locations of liver tumors. In this paper, we propose a novel hybrid end-to-end network, called TD-Net, which incorporates Transformer and direction information into convolution network to segment liver tumor from CT images automatically. The proposed TD-Net is composed of a shared encoder, two decoding branches, four skip connections, and a direction guidance block. The shared encoder is utilized to extract multi-level feature information, and the two decoding branches are respectively designed to produce initial segmentation map and direction information. To preserve spatial information, four skip connections are used to concatenate each encoder layer and its corresponding decoder layer, and in the fourth skip connection a Transformer module is constructed to extract global context. Furthermore, a direction guidance block is well-designed to rectify feature maps to further improve segmentation accuracy. Extensive experiments conducted on public LiTS and 3DIRCADb datasets validate that the proposed TD-Net can effectively segment liver tumor from CT images in an end-to-end manner and its segmentation accuracy surpasses those of many existing methods.
肝脏肿瘤分割在肝细胞癌或转移瘤的诊断和治疗中起着至关重要的作用。然而，由于肝脏肿瘤边界模糊，形状、大小和位置变化较大，准确的自动肿瘤分割仍然是一项具有挑战性的任务。在本文中，我们提出了一种新颖的混合端到端网络，称为TD-Net，它将Transformer和方向信息融入卷积网络中，以自动从CT图像中分割肝脏肿瘤。所提出的TD-Net由一个共享编码器、两个解码分支、四个跳跃连接和一个方向引导块组成。共享编码器用于提取多级特征信息，两个解码分支分别设计用于生成初始分割图和方向信息。为了保留空间信息，使用四个跳跃连接将每个编码器层与其相应的解码器层连接起来，并且在第四个跳跃连接中构建了一个Transformer模块来提取全局上下文。此外，精心设计了一个方向引导块来校正特征图，以进一步提高分割精度。在公开的LiTS和3DIRCADb数据集上进行的大量实验验证了所提出的TD-Net能够以端到端的方式有效地从CT图像中分割肝脏肿瘤，并且其分割精度超过了许多现有方法。
