Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Sebastian Berisha"'
Autor:
Reza Reihanisaransari, Chalapathi Charan Gajjela, Xinyu Wu, Ragib Ishrak, Yanping Zhong, David Mayerich, Sebastian Berisha, Rohith Reddy
Publikováno v:
Chemical & Biomedical Imaging, Vol 2, Iss 9, Pp 651-658 (2024)
Externí odkaz:
https://doaj.org/article/ea4f877d45514a1ab10f680a80c8c438
Autor:
Francisco Contijoch, Yuchi Han, Srikant Kamesh Iyer, Peter Kellman, Gene Gualtieri, Mark A Elliott, Sebastian Berisha, Joseph H Gorman, Robert C Gorman, James J Pilla, Walter R T Witschey
Publikováno v:
PLoS ONE, Vol 15, Iss 12, p e0244286 (2020)
BackgroundSegmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed
Externí odkaz:
https://doaj.org/article/699389d87a414ce89f13e6a4d774aa11
Autor:
Mahsa Lotfollahi, Sebastian Berisha, Leila Saadatifard, Laura Montier, Jokūbas Žiburkus, David Mayerich
Publikováno v:
PLoS ONE, Vol 14, Iss 6, p e0215843 (2019)
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper,
Externí odkaz:
https://doaj.org/article/10093fb41e014376bd297c2e224f022a
Publikováno v:
PLoS ONE, Vol 11, Iss 3, p e0151144 (2016)
PurposeTo develop a robust T1ρ magnetic resonance imaging (MRI) sequence for assessment of myocardial disease in humans.Materials and methodsWe developed a breath-held T1ρ mapping method using a single-shot, T1ρ-prepared balanced steady-state free
Externí odkaz:
https://doaj.org/article/53598771f8af4f8cb3edd95a39335731
Autor:
Chalapathi Charan Gajjela, Matthew Brun, Rupali Mankar, Sara Corvigno, Noah Kennedy, Yanping Zhong, Jinsong Liu, Anil K. Sood, David Mayerich, Sebastian Berisha, Rohith Reddy
Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6077722d900c673f6aef45b64830581c
Publikováno v:
Applied Spectroscopy. 73:556-564
Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::604386b4fbc5d652e0b13809dcad8588
https://doi.org/10.1177/0003702818819857
https://doi.org/10.1177/0003702818819857
Autor:
Mahsa Lotfollahi, Nguyen Tran, Chalapathi Gajjela, Sebastian Berisha, Zhu Han, David Mayerich, Rohith Reddy
Minfrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33248603c7798d2346a10827b3ad9534
http://arxiv.org/abs/2008.00566
http://arxiv.org/abs/2008.00566
Publikováno v:
Hyperspectral Image Analysis ISBN: 9783030386160
Deep neural networks have emerged as a set of robust machine learning tools for computer vision. The suitability of convolutional and recurrent neural networks, along with their variants, is well documented for color image analysis. However, remote s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23b5558fbaf80b7563272f082d60ef13
https://doi.org/10.1007/978-3-030-38617-7_3
https://doi.org/10.1007/978-3-030-38617-7_3
Autor:
S. Kamesh Iyer, Robert C. Gorman, Sebastian Berisha, Joseph H. Gorman, Mark A. Elliott, Walter R Witschey, James J. Pilla, Yuchi Han, G. Gualtierri, Francisco Contijoch, Peter Kellman
Publikováno v:
PloS one, vol 15, iss 12
PLoS ONE, Vol 15, Iss 12, p e0244286 (2020)
PLoS ONE
PLoS ONE, Vol 15, Iss 12, p e0244286 (2020)
PLoS ONE
Background Segmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we develope
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::316a87964ff5d30708b8c1ee27711201
https://escholarship.org/uc/item/4df7806k
https://escholarship.org/uc/item/4df7806k
Autor:
Farideh Foroozandeh Shahraki, Saurabh Prasad, Sebastian Berisha, Leila Saadatifard, Mahsa Lotfollahi, David Mayerich
Publikováno v:
Hyperspectral Image Analysis ISBN: 9783030386160
Deep neural networks are emerging as a popular choice for hyperspectral image analysis—compared with other machine learning approaches, they are more effective for a variety of applications in hyperspectral imaging. Part I (Chap. 3) introduces the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b719be4b630158c06de35bc46cc08d5
https://doi.org/10.1007/978-3-030-38617-7_4
https://doi.org/10.1007/978-3-030-38617-7_4