Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Syed M. S. Reza"'
Autor:
Jennifer Sword, Ji Hyun Lee, Marcelo A. Castro, Jeffrey Solomon, Nina Aiosa, Syed M. S. Reza, Winston T. Chu, Joshua C. Johnson, Christopher Bartos, Kurt Cooper, Peter B. Jahrling, Reed F. Johnson, Claudia Calcagno, Ian Crozier, Jens H. Kuhn, Lisa E. Hensley, Irwin M. Feuerstein, Venkatesh Mani
Publikováno v:
Microbiology Spectrum, Vol 11, Iss 3 (2023)
ABSTRACT Marburg virus (MARV) is a highly virulent zoonotic filovirid that causes Marburg virus disease (MVD) in humans. The pathogenesis of MVD remains poorly understood, partially due to the low number of cases that can be studied, the absence of s
Externí odkaz:
https://doaj.org/article/56c1f92e128642cca1baf58ff48cf89f
Autor:
Khan M. Iftekharuddin, Syed M. S. Reza, Linmin Pei, Christos Davatzikos, Arastoo Vossough, Spyridon Bakas
Publikováno v:
Biomed Signal Process Control
This work proposes a novel framework for brain tumor segmentation prediction in longitudinal multimodal MRI scans, comprising two methods; feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture feature
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89996741defd2e1b381bf6912dd5255e
https://pubmed.ncbi.nlm.nih.gov/34354762
https://pubmed.ncbi.nlm.nih.gov/34354762
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030598600
MLMI@MICCAI
MLMI@MICCAI
Even though state-of-the-art convolutional neural networks (CNNs) have shown outstanding performance in a wide range of imaging applications, they typically require large amounts of high-quality training data to prevent over fitting. In the case of m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c9b89d54a20fba5685587d955e12291
https://doi.org/10.1007/978-3-030-59861-7_58
https://doi.org/10.1007/978-3-030-59861-7_58
Autor:
Chaolu Feng, Paul Suetens, Oskar Maier, Daniel Rueckert, Linmin Pei, Paul Bentley, Christian Ledig, Janina von der Gablentz, Frederik Maes, Matthias Liebrand, Eero Salli, Chris Pal, Richard McKinley, Heinz Handels, Roland Wiest, Jia-Hong Lee, Stefan Winzeck, Ching-Wei Wang, Karl Egger, Francis Dutil, Liang Chen, Jan S. Kirschke, Matthias Wilms, Janaki Raman Rangarajan, Hanna-Leena Halme, Konstantinos Kamnitsas, Ulrike M. Krämer, Levin Häni, Ben Glocker, Michael Götz, Mattias P. Heinrich, Abdul Basit, Mohammad Havaei, David Robben, Hugo Larochelle, Daan Christiaens, Khan M. Iftekharuddin, Syed M. S. Reza, Antti Korvenoja, Bjoern H. Menze, John Muschelli, Qaiser Mahmood, Tom Haeck, Peter Schramm, Thomas F. Münte, Klaus H. Maier-Hein, Elias Kellner, Pierre-Marc Jodoin, Mauricio Reyes
Publikováno v:
Medical Image Analysis. 35:250-269
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::550bedcc1eda99c6d19f16f927decff7
https://doi.org/10.1016/j.media.2016.07.009
https://doi.org/10.1016/j.media.2016.07.009
A glioma grading method using conventional structural magnetic resonance image (MRI) and molecular data from patients is proposed. The noninvasive grading of glioma tumors is obtained using multiple radiomic texture features including dynamic texture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3b2ede88e98f3a4306e3f63fcb792a0
https://europepmc.org/articles/PMC6479231/
https://europepmc.org/articles/PMC6479231/
Publikováno v:
Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging
Chordoma is a rare type of tumor that usually appears in the bone near the spinal cord and skull base. Due to their location in the skull base and diverse appearance in size and shape, automatic segmentation of chordoma tumors from magnetic resonance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1730f7570f148697fec487f6bd2c7647
https://doi.org/10.1117/12.2514000
https://doi.org/10.1117/12.2514000
Publikováno v:
Medical Imaging: Computer-Aided Diagnosis
In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Latti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13d4f611686740e089ce938200367651
https://doi.org/10.1117/12.2254034
https://doi.org/10.1117/12.2254034
Autor:
Syed M. S. Reza, Khan M. Iftekharuddin
Publikováno v:
Medical Imaging: Computer-Aided Diagnosis
This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08faad247cf4e98e1605769285886005
https://pubmed.ncbi.nlm.nih.gov/27942094
https://pubmed.ncbi.nlm.nih.gov/27942094
Publikováno v:
Springer Series in Computational Neuroscience ISBN: 9781493939930
This chapter discusses multi-fractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, non-enhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bba0b1166373b11bdb57ca17a36e466
https://doi.org/10.1007/978-1-4939-3995-4_21
https://doi.org/10.1007/978-1-4939-3995-4_21
Publikováno v:
BIBM
In this work, we propose a novel method to improve the predication of brain tumor growth by fusing with the state-of-art tumor segmentation. The Glioma Image Segmentation and Registration (GLISTR) is known for joint segmentation and deformable regist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::502c31660b9d8a56eb5037b22c4e1f43
https://doi.org/10.1109/bibm.2015.7359719
https://doi.org/10.1109/bibm.2015.7359719