Multi-Modal Tumor Segmentation With Deformable Aggregation and Uncertain Region Inpainting-Reference-Cited by-同舟云学术

Multi-Modal Tumor Segmentation With Deformable Aggregation and Uncertain Region Inpainting

Author:

Zhang Yue¹^ORCID,Peng Chengtao²^ORCID,Tong Ruofeng³^ORCID,Lin Lanfen³^ORCID,Chen Yen-Wei⁴^ORCID,Chen Qingqing⁵^ORCID,Hu Hongjie⁵,Zhou S. Kevin¹^ORCID

Affiliation:

1. School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China

2. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China

3. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

4. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan

5. Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Funder

Jiangsu Funding Program for Excellent Postdoctoral Talent

Natural Science Foundation of China

Open Fund Project of Guangdong Academy of Medical Sciences, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

Reference58 articles.

1. Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks;Christ;arXiv:1702.05970,2017

2. Automatic liver lesion segmentation using a deep convolutional neural network method;Han;arXiv:1704.07239,2017

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