计算机科学
分割
人工智能
编码器
解码方法
图像分割
端到端原则
特征(语言学)
背景(考古学)
计算机视觉
模式识别(心理学)
块(置换群论)
肝母细胞瘤
算法
医学
放射科
古生物学
语言学
哲学
几何学
数学
生物
操作系统
作者
Shuanhu Di,Yuqian Zhao,Miao Liao,Fan Zhang,Xiong Li
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:27 (3): 1163-1172
被引量:14
标识
DOI:10.1109/jbhi.2022.3181974
摘要
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.
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