Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Weili Lin"'
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:
IEEE Access, Vol 7, Pp 33728-33740 (2019)
Hippocampal segmentation from infant brain MR images is indispensable for studying early brain development. However, most of the hippocampal segmentation methods were developed for population-based adult brain images, which are not suitable for longi
Autor:
Mohamed Ali Mahjoub, Weili Lin, Dinggang Shen, Alaa Bessadok, Ahmed Nebli, Islem Rekik, Gang Li
Publikováno v:
Predictive Intelligence in Medicine ISBN: 9783030876012
PRIME@MICCAI
PRIME@MICCAI
Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7dc66a145cb5cd135d549b83a8a35c38
https://doi.org/10.1007/978-3-030-87602-9_2
https://doi.org/10.1007/978-3-030-87602-9_2
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872335
MICCAI (7)
MICCAI (7)
Deep learning based image quality assessment (IQA) is useful for automatic quality control of medical images but requires a large number of training data. Though using multi-site data can significantly increase the training sample size and improve th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7a5e9a45236dbd6e2c5c4ff7ae11107
https://doi.org/10.1007/978-3-030-87234-2_36
https://doi.org/10.1007/978-3-030-87234-2_36
Autor:
Lufan Liao, Yu Li, Xin Zhang, Li Wang, Jiale Cheng, Zhengwang Wu, John H. Gilmore, Hao Ni, Weili Lin, Ruiyan Fang, Dan Hu, Xinyao Ding, Xiangmin Xu, Gang Li
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
MLMI@MICCAI
During infancy, the human brain develops rapidly in terms of structure, function and cognition. The tight connection between cognitive skills and brain morphology motivates us to focus on individual level cognitive score prediction using longitudinal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6c363f839c81db9aa4989f1860a215b
https://doi.org/10.1007/978-3-030-87589-3_24
https://doi.org/10.1007/978-3-030-87589-3_24
Publikováno v:
Medical image analysis. 68
The connectional map of the baby brain undergoes dramatic changes over the first year of postnatal development, which makes its mapping a challenging task, let alone learning how to predict its evolution. Currently, learning models for predicting bra
Publikováno v:
Med Image Comput Comput Assist Interv
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597276
MICCAI (7)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597276
MICCAI (7)
In this paper, we introduce a technique for super-resolution reconstruction of diffusion MRI, harnessing fiber-continuity (FC) as a constraint in a global whole-brain optimization framework. FC is a biologically-motivated constraint that relates orie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cd433683f94b7d7e85ee5b3e7bf9d80
https://europepmc.org/articles/PMC8562653/
https://europepmc.org/articles/PMC8562653/
Autor:
Kim-Han Thung, Geng Chen, Weili Lin, Tiantian Xu, Pew Thian Yap, Haiyong Wu, Xifeng Wang, Ye Wu, Dinggang Shen, Khoi Minh Huynh
Publikováno v:
IEEE Trans Med Imaging
During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To bet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f6129dd6046ee05b6a22748b84074b2
http://arxiv.org/abs/1908.04483
http://arxiv.org/abs/1908.04483
Publikováno v:
IEEE Transactions on Biomedical Engineering. 64:2803-2812
Objective: The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images. Methods: We propose a structured-learning-based segmentation framework to extract the PVSs from
Autor:
Dinggang Shen, Li Wang, Gang Li, Chunfeng Lian, Wei Shao, Liang Sun, Zhengwang Wu, Daoqiang Zhang, Weili Lin
Publikováno v:
Neuroimage
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2dbacb9c4bb27ee5b5fdab3f23ae18a
https://europepmc.org/articles/PMC6602545/
https://europepmc.org/articles/PMC6602545/