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J

John Muschelli

Johns Hopkins University(US)

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233

Papers

6.6K

Citations

37

h-index

68

i10-index

John Muschelli is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Radiomics and Machine Learning in Medical Imaging & Intracerebral and Subarachnoid Hemorrhage Research & Functional Brain Connectivity Studies. The author has an h-index of 37, co-authored 199 publications.

ORCID: 0000-0001-6469-1750

Research Topics

Radiomics and Machine Learning in Medical Imaging Intracerebral and Subarachnoid Hemorrhage Research Functional Brain Connectivity Studies Advanced Neuroimaging Techniques and Applications Acute Ischemic Stroke Management

Papers by this author

works

Safety and efficacy of minimally invasive surgery plus alteplase in intracerebral haemorrhage evacuation (MISTIE): a randomised, controlled, open-label, phase 2 trial

2016 · The Lancet Neurology · 380 citations

https://doi.org/10.1016/s1474-4422(16)30234-4

Daniel F. Hanley Richard E. Thompson John Muschelli Michael Rosenblum

Minimally Invasive Surgery Plus Recombinant Tissue-type Plasminogen Activator for Intracerebral Hemorrhage Evacuation Decreases Perihematomal Edema

2013 · Stroke · 356 citations

https://doi.org/10.1161/strokeaha.111.000411

W. Andrew Mould J. Ricardo Carhuapoma John Muschelli Karen Lane

The ANTsX ecosystem for quantitative biological and medical imaging

2021 · Scientific Reports · 334 citations

https://doi.org/10.1038/s41598-021-87564-6

Nicholas J. Tustison Philip A. Cook Andrew J. Holbrook Hans J. Johnson

PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT

2017 · NeuroImage Clinical · 68 citations

https://doi.org/10.1016/j.nicl.2017.02.007

John Muschelli Elizabeth Sweeney Natalie Ullman Paul Vespa

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

2021 · NeuroImage · 59 citations

https://doi.org/10.1016/j.neuroimage.2021.118703

Mahbaneh Eshaghzadeh Torbati Davneet Minhas Ghasan E Ahmad Erin E. O’Connor

A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis

2018 · NeuroImage Clinical · 23 citations

https://doi.org/10.1016/j.nicl.2018.10.013

Alessandra M. Valcarcel Kristin A. Linn Fariha Khalid Simon Vandekar

Research topics

topics

Radiomics and Machine Learning in Medical Imaging

Intracerebral and Subarachnoid Hemorrhage Research

Functional Brain Connectivity Studies

Advanced Neuroimaging Techniques and Applications

Acute Ischemic Stroke Management

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