中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision

文献类型：期刊论文

作者Wang, Changwei3,4; Xu, Rongtao3,4; Xu, Shibiao2; Meng, Weiliang3,4; Xiao, Jun1; Zhang, Xiaopeng3,4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2023-10-12
页码13
关键词Detailed representation transfer lung nodules segmentation medical images segmentation soft mask
ISSN号2162-237X
DOI10.1109/TNNLS.2023.3315271
通讯作者Xu, Shibiao(shibiaoxu@bupt.edu.cn) ; Meng, Weiliang(weiliang.meng@ia.ac.cn)
英文摘要Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named "soft mask", which has richer and more accurate edge details description and better visualization, and develop a universal automatic soft mask annotation pipeline to deal with different datasets correspondingly. Then, a novel network with detailed representation transfer and soft mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special detailed representation transfer module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images and an adversarial training framework with soft mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation, and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies (https://drive.google.com/file/d/15NNkvDTb_0Ku0IoPsNMHezJR TH1Oi1wm/view?usp=sharing).
WOS关键词SMALL PULMONARY NODULES ; CT ; ALGORITHMS
资助项目National Key Research and Development Program of China[2020YFC2008500] ; National Key Research and Development Program of China[2020YFC2008503] ; National Natural Science Foundation of China[62271074] ; National Natural Science Foundation of China[61972459] ; National Natural Science Foundation of China[62171321] ; National Natural Science Foundation of China[62376271] ; National Natural Science Foundation of China[62365014] ; National Natural Science Foundation of China[52175493] ; National Natural Science Foundation of China[62171157]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001085429500001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/54349]  
专题多模态人工智能系统全国重点实验室
模式识别国家重点实验室_三维可视计算
通讯作者Xu, Shibiao; Meng, Weiliang
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Changwei,Xu, Rongtao,Xu, Shibiao,et al. Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:13.
APA Wang, Changwei,Xu, Rongtao,Xu, Shibiao,Meng, Weiliang,Xiao, Jun,&Zhang, Xiaopeng.(2023).Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13.
MLA Wang, Changwei,et al."Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):13.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。