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Graph-convolutional-network-based interactive prostate segmentation in MR images

文献类型：期刊论文

作者Tian, Zhiqiang6; Li, Xiaojian6; Zheng, Yaoyue6; Chen, Zhang6; Shi, Zhong7,8; Liu, Lizhi1,2; Fei, Baowei3,4,5
刊名MEDICAL PHYSICS
出版日期2020-07-13
关键词graph convolutional network interactive segmentation prostate MR image
ISSN号0094-2405
DOI10.1002/mp.14327
通讯作者Tian, Zhiqiang(tianzq@gmail.com)
英文摘要Purpose Accurate and robust segmentation of the prostate from magnetic resonance (MR) images is extensively applied in many clinical applications in prostate cancer diagnosis and treatment. The purpose of this study is the development of a robust interactive segmentation method for accurate segmentation of the prostate from MR images. Methods We propose an interactive segmentation method based on a graph convolutional network (GCN) to refine the automatically segmented results. An atrous multiscale convolutional neural network (CNN) encoder is proposed to learn representative features to obtain accurate segmentations. Based on the multiscale feature, a GCN block is presented to predict the prostate contour in both automatic and interactive manners. To preserve the prostate boundary details and effectively train the GCN, a contour matching loss is proposed. The performance of the proposed algorithm was evaluated on 41 in-house MR subjects and 30 PROMISE12 test subjects. Result The proposed method yields mean Dice similarity coefficients of 93.8 +/- 1.2% and 94.4 +/- 1.0% on our in-house and PROMISE12 datasets, respectively. The experimental results show that the proposed method outperforms several state-of-the-art segmentation methods. Conclusion The proposed interactive segmentation method based on the GCN can accurately segment the prostate from MR images. Our method has a variety of applications in prostate cancer imaging.
资助项目NSFC[61876148] ; NSFC[81602583] ; Fundamental Research Funds for the Central Universities[XJJ2018254] ; China Postdoctoral Science Foundation[2018M631164]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000547741300001
出版者WILEY
资助机构NSFC ; Fundamental Research Funds for the Central Universities ; China Postdoctoral Science Foundation
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/71085]  
专题中国科学院合肥物质科学研究院
通讯作者Tian, Zhiqiang
作者单位1.State Key Lab Oncol South China, Guangzhou 510060, Peoples R China
2.Sun Yat Sen Univ, Canc Ctr, Ctr Med Imaging & Imageguided Therapy, Guangzhou 510060, Peoples R China
3.Univ Texas Dallas, Dept Bioengn, Richardson, TX 75035 USA
4.Univ Texas Southwestern Med Ctr Dallas, Adv Imaging Res Ctr, Dallas, TX 75080 USA
5.Univ Texas Southwestern Med Ctr Dallas, Dept Radiol, Dallas, TX 75080 USA
6.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China
7.Chinese Acad Sci, Inst Canc & Basic Med, Hangzhou 310022, Peoples R China
8.Univ Chinese Acad Sci, Canc Hosp, Hangzhou 310022, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Zhiqiang,Li, Xiaojian,Zheng, Yaoyue,et al. Graph-convolutional-network-based interactive prostate segmentation in MR images[J]. MEDICAL PHYSICS,2020.
APA Tian, Zhiqiang.,Li, Xiaojian.,Zheng, Yaoyue.,Chen, Zhang.,Shi, Zhong.,...&Fei, Baowei.(2020).Graph-convolutional-network-based interactive prostate segmentation in MR images.MEDICAL PHYSICS.
MLA Tian, Zhiqiang,et al."Graph-convolutional-network-based interactive prostate segmentation in MR images".MEDICAL PHYSICS (2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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