Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Yanling Chi"'
Autor:
Yanling Chi, Yuyu Xu, Huiying Liu, Xiaoxiang Wu, Zhiqiang Liu, Jiawei Mao, Guibin Xu, Weimin Huang
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which comprises a feature network, and a 3D–2D CNN-based registration network. The feature network has handcrafted tex
Externí odkaz:
https://doaj.org/article/a731d7e30cff44ce864b8864b9290882
Autor:
Yanling, Chi, Yuyu, Xu, Huiying, Liu, Xiaoxiang, Wu, Zhiqiang, Liu, Jiawei, Mao, Guibin, Xu, Weimin, Huang
This work proposed a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which consists of a feature network, and a 3D-2D CNN-based registration network. The feature network has handcraft texture feature layers to re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1ef2acdedd5634af04cbbf31ac40965
http://arxiv.org/abs/2305.13855
http://arxiv.org/abs/2305.13855
Autor:
Huiying Liu, Yanling Chi, Jiawei Mao, Xiaoxiang Wu, Zhiqiang Liu, Yuyu Xu, Guibin Xu, Weimin Huang
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
In this paper, we focus on the issue of rigid medical image registration using deep learning. Under ultrasound, the moving of some organs, e.g., liver and kidney, can be modeled as rigid motion. Therefore, when the ultrasound probe keeps stationary,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70a2b382f334c956ba221447d20c4606
https://pubmed.ncbi.nlm.nih.gov/34892122
https://pubmed.ncbi.nlm.nih.gov/34892122
Publikováno v:
ICPR
Segmentation of kidney on CT images is critical to computer-assisted surgical planning for kidney interventional therapy. Segmenting kidney manually is impractical in clinical, automatic segmentation is desirable. U-Net has been successful in medical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99c5b0ea77eeabed7fee3b3c0135d6d8
https://doi.org/10.1109/icpr48806.2021.9412746
https://doi.org/10.1109/icpr48806.2021.9412746
Autor:
Su Huang, Weimin Huang, Ru San Tan, Liang Zhong, Jiayin Zhou, Jun-Mei Zhang, Yanling Chi, Zhiping Lin, Lu Huang
Publikováno v:
EMBC
Coronary artery lumen delineation, to localize and grade stenosis, is an important but tedious and challenging task for coronary heart disease evaluation. Deep learning has recently been successful applied to many applications, including medical imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::276a40139d4f52bb578ec974d3fa5a13
https://doi.org/10.1109/embc.2018.8512328
https://doi.org/10.1109/embc.2018.8512328
Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions
Autor:
Boyang Su, Aileen Mae Lomarda, Jack Wei Chieh Tan, Xiaodan Zhao, Nasrul Bin Ismail, Tian Hai Koh, Ris Low, Aaron Sung Lung Wong, Hua Zou, Khung Keong Yeo, Soo Teik Lim, Terrance Chua, Like Gobeawan, Swee Yaw Tan, Chee Yang Chin, Kay Woon Ho, Yi Su, Jun-Mei Zhang, Philip Wong, Xi Su, Soo-Kng Teo, John Carson Allen, Jonathan Yap, Felix Keng, Yanling Chi, Min Wan, Lohendran Baskaran, Chee Tang Chin, Liang Zhong, Dongsi Shuang, Jiang Ming Fam, Ghassan S. Kassab, Jiayin Zhou, Ru San Tan, Weimin Huang, Weijun Wu
Publikováno v:
International journal of cardiology. 267
Background Computed tomography coronary angiography (CTCA) image analysis enables plaque characterization and non-invasive fractional flow reserve (FFR) calculation. We analyzed various parameters derived from CTCA images and evaluated their associat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c0c1845327881e76d9a1581c637adbd
https://pubmed.ncbi.nlm.nih.gov/29804703
https://pubmed.ncbi.nlm.nih.gov/29804703
Publikováno v:
Journal of Visual Communication and Image Representation. 33:85-93
The amplitude, orientation and phase of monogenic representation are complementary.Central pixel code provide complementary information to the local variation code.The directional texture can more accurately describe image texture.BFLD can reduce the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fd409af51599184043937ac7e5e1a95
https://doi.org/10.1016/j.jvcir.2015.08.014
https://doi.org/10.1016/j.jvcir.2015.08.014
Publikováno v:
EMBC
Segmentation and modeling of hepatic components from pre-operative images is very important for treatment planning and guidance in robot-assisted liver tumor ablation. An in-house developed system for hepatic component segmentation and modeling using
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94b94fd9879d6e6547758f3e30b8b8c2
https://pubmed.ncbi.nlm.nih.gov/29060090
https://pubmed.ncbi.nlm.nih.gov/29060090
Autor:
Weimin Huang, Ru San Tan, Kyaw Kyar Toe, Liang Zhong, Jiayin Zhou, J-M Zhang, S.T. Lim, P. Wong, Yanling Chi
Publikováno v:
EMBC
In this work, we proposed to demonstrate the entire 3D coronary tree using panoramic maximum intensity projection (MIP) of coronary arteries, and to detect and quantify coronary stenosis from computed tomography coronary angiography (CTCA). The perfo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52e1c5c536b9902580aca5e3dcaa355a
https://pubmed.ncbi.nlm.nih.gov/29060821
https://pubmed.ncbi.nlm.nih.gov/29060821
Publikováno v:
IEEE Transactions on Biomedical Engineering. 61:2768-2778
Content-based image retrieval systems for $\hbox{3}$ -D medical datasets still largely rely on $\hbox{2}$ -D image-based features extracted from a few representative slices of the image stack. Most $\hbox{2}$ -D features that are currently used in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::204bf1a5c09ed04d4b94ffbf88f2f9c0
https://doi.org/10.1109/tbme.2014.2329057
https://doi.org/10.1109/tbme.2014.2329057