J
Justin Senseney
Researcher at National Institutes of Health
Publications - 9
Citations - 308
Justin Senseney is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Parallel rendering & Volume rendering. The author has an hindex of 3, co-authored 9 publications receiving 255 citations. Previous affiliations of Justin Senseney include Center for Information Technology.
Papers
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Journal ArticleDOI
Spatially isotropic four-dimensional imaging with dual-view plane illumination microscopy
Yicong Wu,Peter Wawrzusin,Justin Senseney,Robert S. Fischer,Ryan Christensen,Anthony Santella,Andrew York,Peter Winter,Clare M. Waterman,Zhirong Bao,Daniel A. Colón-Ramos,Matthew McAuliffe,Hari Shroff +12 more
TL;DR: A dual-view, plane illumination microscope with improved spatiotemporal resolution is developed by switching illumination and detection between two perpendicular objectives in an alternating duty cycle to study biological systems that require high-speed volumetric visualization and/or low photobleaching.
Proceedings ArticleDOI
Automated segmentation of computed tomography images
TL;DR: An image analysis, visualization, and segmentation system has been developed to assist researchers participating in a world-wide imaging study to determine risk factors contributing to chronic osteoarthritis.
Proceedings ArticleDOI
2D registration guided models for semi-automatic MRI prostate segmentation
Ruida Cheng,Baris Turkbey,Justin Senseney,Marcelino Bernardo,Alexandra Bokinsky,William Gandler,Evan S. McCreedy,Thomas Pohida,Peter L. Choyke,Matthew J. McAuliffe +9 more
TL;DR: In this work, two semi-automatic techniques for segmentation of T2-weighted MRI images of the prostate are presented, based on 2D registration that changes shape to fit the prostate boundary between adjacent slices.
Proceedings ArticleDOI
A flexible Java GPU-enhanced visualization framework and its applications
Ruida Cheng,Justin Senseney,Nishith Pandya,Evan McCreedy,Matthew McAuliffe,Alexandra Bokinsky +5 more
TL;DR: A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets and builds on top of the GPU framework to show the extensibility of the application.
Proceedings ArticleDOI
Segmentation and surface reconstruction model of prostate MRI to improve prostate cancer diagnosis
Ruida Cheng,Marcelino Bernardo,Justin Senseney,Alexandra Bokinsky,William Gandler,Baris Turkbey,Thomas Pohida,Peter L. Choyke,Matthew J. McAuliffe +8 more
TL;DR: In this paper, the authors presented a prostate segmentation and surface reconstruction model for multiparametric MRI with histopathology slides from radical prostatectomy specimens and targeted biopsy specimens.