Cargando…

Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model

Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct...

Descripción completa

Detalles Bibliográficos
Autores principales:	Fan, Shengyu, Bian, Yueyan, Chen, Hao, Kang, Yan, Yang, Qi, Tan, Tao
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2020
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6965699/ https://www.ncbi.nlm.nih.gov/pubmed/31998107 http://dx.doi.org/10.3389/fninf.2019.00077

Ejemplares similares

Segmentation of Cerebrovascular Anatomy from TOF-MRA Using Length-Strained Enhancement and Random Walker
por: Xiao, Ruoxiu, et al.
Publicado: (2020)

Effectiveness of MRA on embolized intracranial aneurysms: a comparison of DSA, CE-MRA, and TOF-MRA
por: Qassim, Ally Mohamed, et al.
Publicado: (2019)

Characterising Eye Movement Events with an Unsupervised Hidden Markov Model
por: Lüken, Malte, et al.
Publicado: (2022)

Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data
por: Masaracchia, Laura, et al.
Publicado: (2023)

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients
por: Roth, Nils, et al.
Publicado: (2021)

Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation
por: Boudaren, Mohamed El Yazid, et al.
Publicado: (2020)

Use of PETRA-MRA to assess intracranial arterial stenosis: Comparison with TOF-MRA, CTA, and DSA
por: Niu, Junxia, et al.
Publicado: (2023)

Multiple Hidden Markov Model for Pathological Vessel Segmentation
por: Hu, Xin, et al.
Publicado: (2018)

Approximating Hidden Gaussian Markov Random Fields
por: Rue, H, et al.
Publicado: (2003)

biomvRhsmm: Genomic Segmentation with Hidden Semi-Markov Model
por: Du, Yang, et al.
Publicado: (2014)

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI
por: Castillo-Barnes, Diego, et al.
Publicado: (2017)

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks
por: Kossen, Tabea, et al.
Publicado: (2022)

Statistical Inference in Hidden Markov Models Using k-Segment Constraints
por: Titsias, Michalis K., et al.
Publicado: (2016)

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes
por: Nakamura, Tomoaki, et al.
Publicado: (2017)

Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns
por: Heerah, Karine, et al.
Publicado: (2017)

Hidden Semi-Markov Models to Segment Reading Phases from Eye Movements
por: Olivier, Brice, et al.
Publicado: (2022)

Inference in Hidden Markov Models
por: Cappe, Olivier
Publicado: (2005)

Hidden Markov Modeling with HMMTeacher
por: Fuentes-Beals, Camilo, et al.
Publicado: (2022)

Optimization of MR Parameters of 3D TOF-MRA for Various Intracranial Stents at 3.0T MRI
por: Choi, Jin Woo, et al.
Publicado: (2011)

LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
por: Malonzo, Maia H., et al.
Publicado: (2023)

Unsupervised learning by competing hidden units
por: Krotov, Dmitry, et al.
Publicado: (2019)

Hidden Markov Model Variants and their Application
por: Winters-Hilt, Stephen
Publicado: (2006)

Hidden Markov models: estimation and control
por: Elliott, Robert J, et al.
Publicado: (1995)

Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models
por: Touloupou, Panayiota, et al.
Publicado: (2019)

Investigating Signal Loss due to a Carotid Artery Stent in 3D-TOF-MRA
por: Kato, Hiroshi, et al.
Publicado: (2020)

Dilated MMA sign in cDAVF and other arterial feeders on 3D TOF MRA
por: Foo, Sin Y, et al.
Publicado: (2021)

Error statistics of hidden Markov model and hidden Boltzmann model results
por: Newberg, Lee A
Publicado: (2009)

Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models
por: Hsiao, Janet H., et al.
Publicado: (2022)

Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge
por: Di Noto, Tommaso, et al.
Publicado: (2022)

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms
por: Kong, Delian, et al.
Publicado: (2023)

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics
por: Zhong, Cheng, et al.
Publicado: (2023)

Epitope Discovery with Phylogenetic Hidden Markov Models
por: Lacerda, Miguel, et al.
Publicado: (2010)

Hidden Markov processes: theory and applications to biology
por: Vidyasagar, M
Publicado: (2014)

Estimating Empirical Codon Hidden Markov Models
por: De Maio, Nicola, et al.
Publicado: (2013)

Scanpath modeling and classification with hidden Markov models
por: Coutrot, Antoine, et al.
Publicado: (2017)

Modelling reassurances of clinicians with hidden Markov models
por: Popov, Valentin, et al.
Publicado: (2019)

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA
por: Ham, Sungwon, et al.
Publicado: (2023)

OTO-Net: An Automated MRA Image Segmentation Network for Intracranial Aneurysms
por: Ye, Jianming, et al.
Publicado: (2022)

Diagnostic yield of TOF-MRA for detecting incidental vascular lesions in patients with cognitive impairment: An observational cohort study
por: Park, Ho Young, et al.
Publicado: (2022)

Morphological characteristics of lenticulostriate arteries in a large age-span population: Results from 7T TOF-MRA
por: Wei, Ning, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_51t2386tth5eo5iq1fscgpeacg