4,387 research outputs found
Soft tissue structure modelling for use in orthopaedic applications and musculoskeletal biomechanics
We present our methodology for the three-dimensional anatomical and geometrical description of soft tissues, relevant for orthopaedic surgical applications and musculoskeletal biomechanics. The technique involves the segmentation and geometrical description of muscles and neurovascular structures from high-resolution computer tomography scanning for the reconstruction of generic anatomical models. These models can be used for quantitative interpretation of anatomical and biomechanical aspects of different soft tissue structures. This approach should allow the use of these data in other application fields, such as musculoskeletal modelling, simulations for radiation therapy, and databases for use in minimally invasive, navigated and robotic surgery
Auto-Grading for 3D Modeling Assignments in MOOCs
Bottlenecks such as the latency in correcting assignments and providing a
grade for Massive Open Online Courses (MOOCs) could impact the levels of
interest among learners. In this proposal for an auto-grading system, we
present a method to simplify grading for an online course that focuses on 3D
Modeling, thus addressing a critical component of the MOOC ecosystem that
affects. Our approach involves a live auto-grader that is capable of attaching
descriptive labels to assignments which will be deployed for evaluating
submissions. This paper presents a brief overview of this auto-grading system
and the reasoning behind its inception. Preliminary internal tests show that
our system presents results comparable to human graders
A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data
Differential phase-contrast computed tomography (DPC-CT) is a powerful
analysis tool for soft-tissue and low-atomic-number samples. Limited by the
implementation conditions, DPC-CT with incomplete projections happens quite
often. Conventional reconstruction algorithms are not easy to deal with
incomplete data. They are usually involved with complicated parameter selection
operations, also sensitive to noise and time-consuming. In this paper, we
reported a new deep learning reconstruction framework for incomplete data
DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT
reconstruction algorithm in the phase-contrast projection sinogram domain. The
estimated result is the complete phase-contrast projection sinogram not the
artifacts caused by the incomplete data. After training, this framework is
determined and can reconstruct the final DPC-CT images for a given incomplete
phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an
example, this framework has been validated and demonstrated with synthetic and
experimental data sets. Embedded with DPC-CT reconstruction, this framework
naturally encapsulates the physical imaging model of DPC-CT systems and is easy
to be extended to deal with other challengs. This work is helpful to push the
application of the state-of-the-art deep learning theory in the field of
DPC-CT
A Semantic Portal for the International Affairs Sector
The Royal Institute Elcano(dagger) (Real Instituto Elcano) in Spain is a prestigious independent political institute whose mission is to comment on the political situation in the world focusing on its relation to Spain. As part of its dissemination strategy it operates a public website. The online content can be accessed by navigating through categories or by a keyword-based, full text search engine. The work described in this paper aims at improving access to the content. We describe an approach, tools and techniques that allow building a semantic portal, where access is based on the meaning of concepts and relations of the International Affairs domain. The approach comprises an automatic ontology-based annotator, a semantic search engine with a natural language inter-face, a web publication tool allowing semantic navigation, and a 3D visualization component. The semantic portal is currently being tested by the Institute
GPU accelerated real-time multi-functional spectral-domain optical coherence tomography system at 1300 nm.
We present a GPU accelerated multi-functional spectral domain optical coherence tomography system at 1300 nm. The system is capable of real-time processing and display of every intensity image, comprised of 512 pixels by 2048 A-lines acquired at 20 frames per second. The update rate for all four images with size of 512 pixels by 2048 A-lines simultaneously (intensity, phase retardation, flow and en face view) is approximately 10 frames per second. Additionally, we report for the first time the characterization of phase retardation and diattenuation by a sample comprised of a stacked set of polarizing film and wave plate. The calculated optic axis orientation, phase retardation and diattenuation match well with expected values. The speed of each facet of the multi-functional OCT CPU-GPU hybrid acquisition system, intensity, phase retardation, and flow, were separately demonstrated by imaging a horseshoe crab lateral compound eye, a non-uniformly heated chicken muscle, and a microfluidic device. A mouse brain with thin skull preparation was imaged in vivo and demonstrated the capability of the system for live multi-functional OCT visualization
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