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Full-Text Articles in Medical Specialties
Medical Imaging Applications Of Federated Learning, Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, Alireza Akhbardeh
Medical Imaging Applications Of Federated Learning, Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, Alireza Akhbardeh
Journal Articles
Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, …
Improving The Reliability And Accessibility Of Ct Perfusion Imaging In Acute Ischemic Stroke, Kevin J. Chung
Improving The Reliability And Accessibility Of Ct Perfusion Imaging In Acute Ischemic Stroke, Kevin J. Chung
Electronic Thesis and Dissertation Repository
CT perfusion (CTP) imaging is a validated treatment decision support tool in acute ischemic stroke. Automated analysis of CTP cerebral blood flow (CBF) and Tmax maps produces estimates of ischemic core and penumbra volumes used to determine target mismatch profiles for treatment. However, availability and utilization of CTP is low due to diagnostic variability between CTP software and technical, logistical, and radiation dose considerations that may limit its routine adoption. The objective of this doctoral research was to improve the reliability and accessibility of CTP by (1) improving diagnostic agreement between CTP software, (2) enabling perfusion imaging with standard acute …
Comparison Of Physics-Based Deformable Registration Methods For Image-Guided Neurosurgery, Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, Ron Kikinis
Comparison Of Physics-Based Deformable Registration Methods For Image-Guided Neurosurgery, Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, Ron Kikinis
Computer Science Faculty Publications
This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete …