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Joint Transformer and Multi-scale CNN for DCE-MRI Breast Cancer Segmentation

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成果类型:
期刊论文
作者:
Qin, Chuanbo;Wu, Yujie;Zeng, Junying;Tian, Lianfang;Zhai, Yikui;...
通讯作者:
Junying Zeng
作者机构:
[Qin, Chuanbo; Wu, Yujie; Zeng, Junying; Zhai, Yikui] Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
[Li, Fang] Automation Science and Engineering, South China University of Technology, Guangzhou, China
[Fang Li] Jiangmen Maternal and Child Healthcare Hospital, Jiangmen, China
[Zhang, Xiaozhi] School of Electrical Engineering, University of South China, Hengyang, China
通讯机构:
[Junying Zeng] F
Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
语种:
英文
关键词:
DCE-MRI;Segmentation;Breast tumor;U-net;Transformer;Dynamic ReLU
期刊:
Soft Computing
ISSN:
1432-7643
年:
2022
卷:
26
期:
17
页码:
8317-8334
基金类别:
This study is supported by the National Nature Science Foundation of China (No.62071213), Special Project in key Areas of Artificial Intelligence in Guangdong Universities (No.2019KZDZX1017), Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515010716), and open fund of Guangdong Key Laboratory of digital signal and image processing technology (2019GDDSIPL-03, 2020GDDSIPL-03).
机构署名:
本校为其他机构
院系归属:
电气工程学院
摘要:
Automatic segmentation of breast cancer lesions in dynamic contrast-enhanced magnetic resonance imaging is challenged by low accuracy of delineation of the infiltration area, variable structure and shapes, large intensity heterogeneity changes, and low boundary contrast. This study constructed a two-stage breast cancer image segmentation framework and proposes a novel breast cancer lesion segmentation model (TR-IMUnet). The benchmark U-Net network model enables a rough delineation of the breast area in the acquired images and eliminates the inf...

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