Výsledky vyhledávání - "Blaber, Justin A."

Report

MRI correlates of chronic symptoms in mild traumatic brain injury

Autor: Kerley, Cailey I., Schilling, Kurt G., Blaber, Justin, Miller, Beth, Newton, Allen, Anderson, Adam W., Landman, Bennett A., Rex, Tonia S.

Veterans with mild traumatic brain injury (mTBI) have reported auditory and visual dysfunction that persists beyond the acute incident. The etiology behind these symptoms is difficult to characterize with current clinical imaging. These functional de

Externí odkaz: http://arxiv.org/abs/1912.04116

Zobrazit plný text záznamu

Report

Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution

Autor: Nath, Vishwesh, Schilling, Kurt G., Hansen, Colin B., Parvathaneni, Prasanna, Hainline, Allison E., Bermudez, Camilo, Plassard, Andrew J., Janve, Vaibhav, Gao, Yurui, Blaber, Justin A., Stępniewska, Iwona, Anderson, Adam W., Landman, Bennett A.

Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion weighted MRI models, e.g., constrained spherical deconvolution (CSD).

Externí odkaz: http://arxiv.org/abs/1911.07927

Zobrazit plný text záznamu

Report

Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning

Autor: Bermudez, Camilo, Blaber, Justin, Remedios, Samuel W., Reynolds, Jess E., Lebel, Catherine, McHugo, Maureen, Heckers, Stephan, Huo, Yuankai, Landman, Bennett A.

Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) approach ha

Externí odkaz: http://arxiv.org/abs/1908.04702

Zobrazit plný text záznamu

Report

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Autor: Remedios, Samuel, Roy, Snehashis, Blaber, Justin, Bermudez, Camilo, Nath, Vishwesh, Patel, Mayur B., Butman, John A., Landman, Bennett A., Pham, Dzung L.

Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer prote

Externí odkaz: http://arxiv.org/abs/1903.04207

Zobrazit plný text záznamu

Report

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning

Autor: Nath, Vishwesh, Parvathaneni, Prasanna, Hansen, Colin B., Hainline, Allison E., Bermudez, Camilo, Remedios, Samuel, Blaber, Justin A., Schilling, Kurt G., Lyu, Ilwoo, Janve, Vaibhav, Gao, Yurui, Stepniewska, Iwona, Rogers, Baxter P., Newton, Allen T., Davis, L. Taylor, Luci, Jeff, Anderson, Adam W., Landman, Bennett A.

Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) a

Externí odkaz: http://arxiv.org/abs/1810.04260

Zobrazit plný text záznamu