Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Weili Lin"'
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 Cybernetics. 49:1123-1136
Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6–8 months of age), white matter and gray mat
Autor:
Qian Zhang, Josien P. W. Pluim, Jing Xia, Ismail Ben Ayed, Guannan Li, Jitae Shin, Jia-Wei Chen, Zhengwang Wu, Adrià Casamitjana, Vladimir S. Fonov, Yongchao Xu, Weili Lin, Fan Wang, Li Wang, Jose Dolz, Oualid Benkarim, Guoyan Zheng, Guodong Zeng, Christian Desrosiers, Elodie Puybareau, Dong Nie, Gerard Sanroma, Gang Li, Kim-Han Thung, Andrew Doyle, Pim Moeskops, Dinggang Shen, Toan Duc Bui, Verónica Vilaplana
Publikováno v:
IEEE Transactions on Medical Imaging, 38(9):2901712, 2219-2230. Institute of Electrical and Electronics Engineers
Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neuro
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:
Li Wang, Yuchen Pei, Ya Wang, Dan Hu, Yue Sun, Weili Lin, Liangjun Chen, Gang Li, Zhengwang Wu, Fenqiang Zhao
Publikováno v:
Med Image Comput Comput Assist Interv
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872014
MICCAI (4)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872014
MICCAI (4)
Longitudinal infant dedicated cerebellum atlases play a fundamental role in characterizing and understanding the dynamic cerebellum development during infancy. However, due to the limited spatial resolution, low tissue contrast, tiny folding structur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29419589b2efa07077dd2956a72c1edd
https://europepmc.org/articles/PMC8817766/
https://europepmc.org/articles/PMC8817766/
Publikováno v:
ISBI
Proc IEEE Int Symp Biomed Imaging
Proc IEEE Int Symp Biomed Imaging
Due to the extremely low intensity contrast between the white matter (WM) and the gray matter (GM) at around 6 months of age (the isointense phase), it is difficult for manual annotation, hence the number of training labels is highly limited. Consequ
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030598600
MLMI@MICCAI
Mach Learn Med Imaging
MLMI@MICCAI
Mach Learn Med Imaging
To characterize early cerebellum development, accurate segmentation of the cerebellum into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) tissues is one of the most pivotal steps. However, due to the weak tissue contrast, extremel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d8423f0d60111eefb775a8aa769c856
https://doi.org/10.1007/978-3-030-59861-7_67
https://doi.org/10.1007/978-3-030-59861-7_67
Autor:
Dan Hu, Dinggang Shen, Li Wang, Zhengwang Wu, Gang Li, Ya Wang, Weili Lin, Liangjun Chen, Zhanhao Mo
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597276
MICCAI (7)
Med Image Comput Comput Assist Interv
MICCAI (7)
Med Image Comput Comput Assist Interv
Accurate subcortical segmentation of infant brain magnetic resonance (MR) images is crucial for studying early subcortical structural growth patterns and related diseases diagnosis. However, dynamic intensity changes, low tissue contrast, and small s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b1fe992daa86cb19267dc14f756e19e
https://doi.org/10.1007/978-3-030-59728-3_63
https://doi.org/10.1007/978-3-030-59728-3_63
Publikováno v:
Biomed Signal Process Control
Accurate segmentation of white matter, gray matter and cerebrospinal fluid from neonatal brain MR images is of great importance in characterizing early brain development. Deep-learning-based methods have been successfully applied to neonatal brain MR
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