SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation-Reference-Cited by-同舟云学术

SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation

Published:2024-05 Issue:5 Volume:28 Page:2854-2865
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Wang Kai-Ni¹^ORCID,Li Sheng-Xiao¹^ORCID,Bu Zhenyu¹^ORCID,Zhao Fu-Xing¹^ORCID,Zhou Guang-Quan¹^ORCID,Zhou Shou-Jun²^ORCID,Chen Yang³^ORCID

Affiliation:

1. School of Biological Science and Medical Engineering, Southeast University, Nanjing, China

2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

3. Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, the School of Computer Science and Engineering, The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Jiangsu Provincial R&D Program

Shenzhen Technology Innovation Commission

Shenzhen Engineering Laboratory for Diagnosis and Treatment Key Technologies of Interventional Surgical Robots

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10521573/10457551.pdf?arnumber=10457551

Reference45 articles.

1. Cancer statistics, 2022

2. A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes

3. Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation

4. Interactive liver tumor segmentation from ct scans using support vector classification with watershed

5. U-Net: Convolutional Networks for Biomedical Image Segmentation

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