Show simple item record Nguyen, Alan Pierre, Yvan Jr. Snapp-Childs, Winona Birch, Scott 2019-07-25T16:33:40Z 2019-07-25T16:33:40Z 2019-07-28
dc.description.abstract Computed tomography (CT) is a diagnostic imaging test using x-rays to create multiple detailed diagnostic images of internal organs, bones, soft tissue and blood vessels. It produces a data set of thin, cross-sectional ``slices'' for viewing and is much more detailed than conventional radiography. Clinicians use CT examination to diagnose cancers, detect abnormal blood vessels, discover disorders of the abdomen, bones, and joints, and to plan surgical interventions such as heart defect or vascular repair. Dedicated visualization workstations allow radiologists to make high-resolution examinations of the data for diagnoses, but understanding the image stacks can be challenging for clinicians without specialized skills, training, and experience. To aid and enhance diagnostic evaluation, we explored a cloud-based workflow using Jetstream. CT data sets were segmented or translated into regions-of-interest (ROI) and/or volumetric 3D reconstructions which were then exported as polygonal 3D surface models. Using data sets obtained via CT from a variety of animal species, this project focused on the process of compiling a medical imaging/segmentation workstation instance with open source software on Jetstream, importing sample data sets into the imaging software, viewing 2D image sequences volumetrically, setting custom transfer functions based on tissue density, and segmenting the anatomy into multiple ROI for export as stereolithography files. Post-processing and polygon mesh editing techniques such as smoothing, transient reduction, and decimation were employed as the model was optimized for 3D printing or online distribution. Results were rendered into 2D graphical representations, and the 3D models were deployed into interactive or virtual reality environments, or were additively-manufactured (3D printed) into real-world objects for visual and tactile examination. After workflows were verified and vetted, the Jetstream medical segmentation VMs were made available for others to view and/or segment their own volumetric data sets. en
dc.language.iso en en
dc.rights.uri en
dc.subject Machine learning en
dc.subject Cloud computing en
dc.subject Neural Networks en
dc.subject CNN en
dc.subject 3D Segmentation en
dc.subject CT scans en
dc.subject DICOM en
dc.subject ParaView en
dc.subject 3D Slicer en
dc.title Visualizing veterinary medical data sets with Jetstream en
dc.type Presentation en
dc.identifier.doi 10.5967/am1a-fv09

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