Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Weili Lin"'
Publikováno v:
Intelligent Medicine.
Autor:
Li Wang, Gang Li, Zhengwang Wu, Fenqiang Zhao, Fan Wang, Weili Lin, Dinggang Shen, Shunren Xia
Publikováno v:
IEEE Trans Med Imaging
Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to faci
Publikováno v:
Med Image Comput Comput Assist Interv
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
The difficulty of acquiring resting-state fMRI of early developing children under the same condition leads to a dedicated protocol, i.e., scanning younger infants during sleep and older children during being awake, respectively. However, the obviousl
Autor:
Li Zhao, Toan Duc Bui, Qi Dou, Yu Zhang, Sijie Niu, Trung Le Phan, Guannan Li, Longchuan Li, Sarah Shultz, Xiaopeng Zong, Wenao Ma, Gang Li, Yue Sun, Ying Wei, Xue Feng, Mallappa Kumara Swamy, Camilo Bermudez Noguera, Tao Zhong, Valerie Jewells, Li Wang, Weili Lin, Ramesh Basnet, Caizi Li, M. Omair Ahmad, Dinggang Shen, Zhihao Lei, Ian H. Gotlib, Kathryn L. Humphreys, Jun Ma, Bennett A. Landman, Jitae Shin, Kun Gao, Zhengwang Wu, Lequan Yu, Xiaoping Yang
Publikováno v:
IEEE Trans Med Imaging
To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods
Publikováno v:
Mach Learn Med Imaging
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
Accurate tissue segmentation of large-scale pediatric brain MR images from multiple sites is essential to characterize early brain development. Due to imaging motion/Gibbs artifacts and multi-site issue (or domain shift issue), it remains a challenge
Publikováno v:
IEEE transactions on medical imaging
Fast and automated image quality assessment (IQA) for diffusion MR images is a crucial step for swiftly making a rescan decision during or after the scanning session. However, learning a model for this task is challenging as the number of annotated d
Publikováno v:
Hybrid PET/MR Neuroimaging ISBN: 9783030823665
Hybrid PET/MR Neuroimaging
Hybrid PET/MR Neuroimaging
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55f9d6f900b272a216685fbd7225d64c
https://doi.org/10.1007/978-3-030-82367-2_9
https://doi.org/10.1007/978-3-030-82367-2_9
Publikováno v:
IEEE Trans Med Imaging
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampl
Publikováno v:
Med Image Anal
Diffusion MRI (DMRI) is a powerful tool for studying early brain development and disorders. However, the typically low spatio-angular resolution of DMRI diminishes structural details and limits quantitative analysis to simple diffusion models. This p
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine learning driven techniques for RAC are predominantly based on supervised learning and therefore practical utility can be limi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c52fbf337832990ab41df00678937373
http://arxiv.org/abs/2110.04604
http://arxiv.org/abs/2110.04604