390 research outputs found
Qualitative, comparative, and collaborative research at large scale: The GENNOVATE field methodology
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
We present a data-driven generative framework for synthesizing blood vessel
3D geometry. This is a challenging task due to the complexity of vascular
systems, which are highly variating in shape, size, and structure. Existing
model-based methods provide some degree of control and variation in the
structures produced, but fail to capture the diversity of actual anatomical
data. We developed VesselVAE, a recursive variational Neural Network that fully
exploits the hierarchical organization of the vessel and learns a
low-dimensional manifold encoding branch connectivity along with geometry
features describing the target surface. After training, the VesselVAE latent
space can be sampled to generate new vessel geometries. To the best of our
knowledge, this work is the first to utilize this technique for synthesizing
blood vessels. We achieve similarities of synthetic and real data for radius
(.97), length (.95), and tortuosity (.96). By leveraging the power of deep
neural networks, we generate 3D models of blood vessels that are both accurate
and diverse, which is crucial for medical and surgical training, hemodynamic
simulations, and many other purposes.Comment: Accepted for MICCAI 202
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the
complexity of vascular systems, which are highly variating in shape, size,
and structure. Existing model-based methods provide some degree of
control and variation in the structures produced, but fail to capture the
diversity of actual anatomical data. We developed VesselVAE, a recursive
variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch
connectivity along with geometry features describing the target surface.
After training, the VesselVAE latent space can be sampled to generate
new vessel geometries. To the best of our knowledge, this work is the
first to utilize this technique for synthesizing blood vessels. We achieve
similarities of synthetic and real data for radius (.97), length (.95), and
tortuosity (.96). By leveraging the power of deep neural networks, we
generate 3D models of blood vessels that are both accurate and diverse,
which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
Keywords: Vascular 3D model
Qualitative, comparative and collaborative research at large scale:The GENNOVATE Field Methodology
We present a field-tested “medium-n” qualitative comparative methodology, which enhances understanding of the strong and fluid influence of gender norms on processes of local agricultural innovation in the Global South. The GENNOVATE approach (“Enabling Gender Equality in Agricultural and Environmental Innovation”) weaves together three broad methodological challenges—context, comparison, and collaboration—and highlights how addressing the social context of innovation contributes to applied research. We discuss GENNOVATE’s analytic approach, sampling framework, data collection, and analysis procedures, and reflect critically on the research strategies adopted to document and learn from the perspectives and experiences of over 7,000 women and men in 137 villages across 26 low- and middle-income countries
Investigation of Gearbox Vibration Transmission Paths on Gear Condition Indicator Performance
Helicopter health monitoring systems use vibration signatures generated from damaged components to identify transmission faults. For damaged gears, these signatures relate to changes in dynamics due to the meshing of the damaged tooth. These signatures, referred to as condition indicators (CI), can perform differently when measured on different systems, such as a component test rig, or a full-scale transmission test stand, or an aircraft. These differences can result from dissimilarities in systems design and environment under dynamic operating conditions. The static structure can also filter the response between the vibration source and the accelerometer, when the accelerometer is installed on the housing. To assess the utility of static vibration transfer paths for predicting gear CI performance, measurements were taken on the NASA Glenn Spiral Bevel Gear Fatigue Test Rig. The vibration measurements were taken to determine the effect of torque, accelerometer location and gearbox design on accelerometer response. Measurements were taken at the housing and compared while impacting the gear set near mesh. These impacts were made at gear mesh to simulate gear meshing dynamics. Data measured on a helicopter gearbox installed in a static fixture were also compared to the test rig. The behavior of the structure under static conditions was also compared to CI values calculated under dynamic conditions. Results indicate that static vibration transfer path measurements can provide some insight into spiral bevel gear CI performance by identifying structural characteristics unique to each system that can affect specific CI response
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