Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Bian, Zhangxing"'
Autor:
Feng, Anqi, Bian, Zhangxing, Dewey, Blake E., Colinco, Alexa Gail, Zhuo, Jiachen, Prince, Jerry L.
Accurate segmentation of thalamic nuclei is important for better understanding brain function and improving disease treatment. Traditional segmentation methods often rely on a single T1-weighted image, which has limited contrast in the thalamus. In t
Externí odkaz:
http://arxiv.org/abs/2409.06897
Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registratio
Externí odkaz:
http://arxiv.org/abs/2403.05111
Autor:
Bian, Zhangxing, Alshareef, Ahmed, Wei, Shuwen, Chen, Junyu, Wang, Yuli, Woo, Jonghye, Pham, Dzung L., Zhuo, Jiachen, Carass, Aaron, Prince, Jerry L.
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-proce
Externí odkaz:
http://arxiv.org/abs/2401.17571
Annotating biomedical images for supervised learning is a complex and labor-intensive task due to data diversity and its intricate nature. In this paper, we propose an innovative method, the efficient one-pass selective annotation (EPOSA), that signi
Externí odkaz:
http://arxiv.org/abs/2308.13649
Autor:
Bian, Zhangxing, Wei, Shuwen, Liu, Yihao, Chen, Junyu, Zhuo, Jiachen, Xing, Fangxu, Woo, Jonghye, Carass, Aaron, Prince, Jerry L.
Tagged magnetic resonance imaging (tMRI) has been employed for decades to measure the motion of tissue undergoing deformation. However, registration-based motion estimation from tMRI is difficult due to the periodic patterns in these images, particul
Externí odkaz:
http://arxiv.org/abs/2308.02949
Autor:
Chen, Junyu, Liu, Yihao, Wei, Shuwen, Bian, Zhangxing, Subramanian, Shalini, Carass, Aaron, Prince, Jerry L., Du, Yong
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image reg
Externí odkaz:
http://arxiv.org/abs/2307.15615
Landmark detection is a critical component of the image processing pipeline for automated aortic size measurements. Given that the thoracic aorta has a relatively conserved topology across the population and that a human annotator with minimal traini
Externí odkaz:
http://arxiv.org/abs/2304.07607
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce severe geome
Externí odkaz:
http://arxiv.org/abs/2304.00217
Autor:
Feng, Anqi, Xue, Yuan, Wang, Yuli, Yan, Chang, Bian, Zhangxing, Shao, Muhan, Zhuo, Jiachen, Gullapalli, Rao P., Carass, Aaron, Prince, Jerry L.
Data-driven thalamic nuclei parcellation depends on high-quality manual annotations. However, the small size and low contrast changes among thalamic nuclei, yield annotations that are often incomplete, noisy, or ambiguously labelled. To train a robus
Externí odkaz:
http://arxiv.org/abs/2303.17706
Autor:
Bian, Zhangxing, Shao, Muhan, Zhuo, Jiachen, Gullapalli, Rao P., Carass, Aaron, Prince, Jerry L.
Connectivity information derived from diffusion-weighted magnetic resonance images~(DW-MRIs) plays an important role in studying human subcortical gray matter structures. However, due to the $O(N^2)$ complexity of computing the connectivity of each v
Externí odkaz:
http://arxiv.org/abs/2302.09247