Bridging the Gap Between 2D and 3D Contexts in CT Volume for Liver and Tumor Segmentation-Reference-Cited by-同舟云学术

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Bridging the Gap Between 2D and 3D Contexts in CT Volume for Liver and Tumor Segmentation

Published:2021-09 Issue:9 Volume:25 Page:3450-3459
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Song Lei^ORCID,Wang Haoqian^ORCID,Wang Z. Jane^ORCID

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6221020/9528502/09416736.pdf?arnumber=9416736

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SCPMan: Shape context and prior constrained multi-scale attention network for pancreatic segmentation;Expert Systems with Applications;2024-10

2. Adaptive Hounsfield Scale Windowing in Computed Tomography Liver Segmentation;International Conference on Information Systems Development;2024-09-09

3. SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation;IEEE Journal of Biomedical and Health Informatics;2024-05

4. Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images;Computers in Biology and Medicine;2024-05

5. A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging;IEEE Journal of Biomedical and Health Informatics;2024-04

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