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Related papers: Superficial White Matter Analysis: An Efficient Po…

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White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts to enable quantification and visualization. Most parcellation methods focus on the deep white matter (DWM), while fewer methods…

Computer Vision and Pattern Recognition · Computer Science 2022-02-01 Tengfei Xue , Fan Zhang , Chaoyi Zhang , Yuqian Chen , Yang Song , Nikos Makris , Yogesh Rathi , Weidong Cai , Lauren J. O'Donnell

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Yiqiong Yang , Yitian Yuan , Baoxing Ren , Ye Wu , Yanqiu Feng , Xinyuan Zhang

Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation. Current methods primarily use the geometric information of fibers (i.e., the spatial trajectories) to group similar fibers…

Image and Video Processing · Electrical Eng. & Systems 2024-12-17 Jin Wang , Bocheng Guo , Yijie Li , Junyi Wang , Yuqian Chen , Jarrett Rushmore , Nikos Makris , Yogesh Rathi , Lauren J O'Donnell , Fan Zhang

Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype classifications. However, parcellation does not always reach…

Computer Vision and Pattern Recognition · Computer Science 2024-09-23 Yui Lo , Yuqian Chen , Fan Zhang , Dongnan Liu , Leo Zekelman , Suheyla Cetin-Karayumak , Yogesh Rathi , Weidong Cai , Lauren J. O'Donnell

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white…

Computer Vision and Pattern Recognition · Computer Science 2023-07-11 Yuqian Chen , Chaoyi Zhang , Tengfei Xue , Yang Song , Nikos Makris , Yogesh Rathi , Weidong Cai , Fan Zhang , Lauren J. O'Donnell

White matter fiber clustering (WMFC) enables parcellation of white matter tractography for applications such as disease classification and anatomical tract segmentation. However, the lack of ground truth and the ambiguity of fiber data (the…

Computer Vision and Pattern Recognition · Computer Science 2021-07-13 Yuqian Chen , Chaoyi Zhang , Yang Song , Nikos Makris , Yogesh Rathi , Weidong Cai , Fan Zhang , Lauren J. O'Donnell

The brain white matter consists of a set of tracts that connect distinct regions of the brain. Segmentation of these tracts is often needed for clinical and research studies. Diffusion-weighted MRI offers unique contrast to delineate these…

Image and Video Processing · Electrical Eng. & Systems 2023-07-06 Hamza Kebiri , Ali Gholipour , Meritxell Bach Cuadra , Davood Karimi

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume…

Computer Vision and Pattern Recognition · Computer Science 2019-10-18 Itay Benou , Tammy Riklin-Raviv

White matter tractography is an advanced neuroimaging technique that reconstructs the 3D white matter pathways of the brain from diffusion MRI data. It can be framed as a pathfinding problem aiming to infer neural fiber trajectories from…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Itzik Waizman , Yakov Gusakov , Itay Benou , Tammy Riklin Raviv

Superficial white matter (SWM) has been less studied than long-range connections despite being of interest to clinical research, andfew tractography parcellation methods have been adapted to SWM. Here, we propose an efficient geometry-based…

Image and Video Processing · Electrical Eng. & Systems 2023-03-03 Nabil Vindas , Nicole Labra Avila , Fan Zhang , Tengfei Xue , Lauren J. O'Donnell , Jean-François Mangin

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcellation, posing challenges for large-scale studies. We…

Image and Video Processing · Electrical Eng. & Systems 2025-06-12 Marcus J. Vroemen , Yuqian Chen , Yui Lo , Tengfei Xue , Weidong Cai , Fan Zhang , Josien P. W. Pluim , Lauren J. O'Donnell

Tractography fiber clustering using diffusion MRI (dMRI) is a crucial method for white matter (WM) parcellation to enable analysis of brains structural connectivity in health and disease. Current fiber clustering strategies primarily use…

Image and Video Processing · Electrical Eng. & Systems 2025-11-04 Bocheng Guo , Jin Wang , Yijie Li , Junyi Wang , Mingyu Gao , Puming Feng , Yuqian Chen , Jarrett Rushmore , Nikos Makris , Yogesh Rathi , Lauren J O'Donnell , Fan Zhang

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using…

Quantitative Methods · Quantitative Biology 2021-04-26 Fan Zhang , Alessandro Daducci , Yong He , Simona Schiavi , Caio Seguin , Robert Smith , Chun-Hung Yeh , Tengda Zhao , Lauren J. O'Donnell

Diffusion magnetic resonance imaging, a non-invasive tool to infer white matter fiber connections, produces a large number of streamlines containing a wealth of information on structural connectivity. The size of these tractography outputs…

Computer Vision and Pattern Recognition · Computer Science 2018-04-17 Kuldeep Kumar , Kaleem Siddiqi , Christian Desrosiers

Registration of diffusion MRI tractography is an essential step for analyzing group similarities and variations in the brain's white matter (WM). Streamline-based registration approaches can leverage the 3D geometric information of fiber…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Junyi Wang , Mubai Du , Ye Wu , Yijie Li , William M. Wells , Lauren J. O'Donnell , Fan Zhang

Streamline classification is essential to identify anatomically meaningful white matter tracts from diffusion MRI (dMRI) tractography. However, current streamline classification methods rely primarily on the geometric features of the…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Haotian Yan , Bocheng Guo , Jianzhong He , Nir A. Sochen , Ofer Pasternak , Lauren J O'Donnell , Fan Zhang

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are compared…

Image and Video Processing · Electrical Eng. & Systems 2023-07-12 Davood Karimi , Hamza Kebiri , Ali Gholipour

Shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and clinical phenotypes. However, conventional methods for…

Image and Video Processing · Electrical Eng. & Systems 2025-10-22 Yui Lo , Yuqian Chen , Dongnan Liu , Leo Zekelman , Jarrett Rushmore , Yogesh Rathi , Nikos Makris , Alexandra J. Golby , Fan Zhang , Weidong Cai , Lauren J. O'Donnell

The state-of-the-art method for automatically segmenting white matter bundles in diffusion-weighted MRI is tractography in conjunction with streamline cluster selection. This process involves long chains of processing steps which are not…

Computer Vision and Pattern Recognition · Computer Science 2017-03-08 Jakob Wasserthal , Peter F. Neher , Fabian Isensee , Klaus H. Maier-Hein

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that…

Methodology · Statistics 2015-06-18 Brian P. Kent , Alessandro Rinaldo , Fang-Cheng Yeh , Timothy Verstynen
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