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Articles 1 - 3 of 3
Full-Text Articles in Medicine and Health Sciences
Killing Adherent And Nonadherent Cancer Cells With The Plasma Pencil, Mounir Laroussi, Soheila Mohades, Nazir Barekzi
Killing Adherent And Nonadherent Cancer Cells With The Plasma Pencil, Mounir Laroussi, Soheila Mohades, Nazir Barekzi
Electrical & Computer Engineering Faculty Publications
The application of low temperature plasmas in biology and medicine may lead to a paradigm shift in the way various diseases can be treated without serious side effects. Low temperature plasmas generated in gas mixtures that contain oxygen or air produce several chemically reactive species that have important biological implications when they interact with eukaryotic or prokaryotic cells. Here, a review of the effects of low temperature plasma generated by the plasma pencil on different cancerous cells is presented. Results indicate that plasma consistently shows a delayed killing effect that is dose dependent. In addition, there is some evidence that …
The Multimodal Brain Tumor Image Segmentation Benchmark (Brats), Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Khan M. Iftekharuddin, Syed M.S. Reza
The Multimodal Brain Tumor Image Segmentation Benchmark (Brats), Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Khan M. Iftekharuddin, Syed M.S. Reza
Electrical & Computer Engineering Faculty Publications
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions …
A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)
A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)
Electrical & Computer Engineering Faculty Publications
MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.
We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …