1,638 research outputs found
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning
Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and
direct surgical procedures, and to track the development of bone-related diseases. This often
involves radiologists who have to annotate bones manually or in a semi-automatic way, which is
a time consuming task. Their annotation workload can be reduced by automated segmentation
and detection of individual bones. This automation of distinct bone segmentation not only has
the potential to accelerate current workflows but also opens up new possibilities for processing
and presenting medical data for planning, navigation, and education.
In this thesis, we explored the use of deep learning for automating the segmentation of all
individual bones within an upper-body CT scan. To do so, we had to find a network architec-
ture that provides a good trade-off between the problem’s high computational demands and the
results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out
to eliminate the most prevalent types of error. To do so, we introduced an novel method called
binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin-
guishing bone from non-bone is conducted separately from identifying the individual bones.
Both predictions are then merged, which leads to superior results. Another type of error is tack-
led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger
fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input
into the network while keeping the growth of additional pixels in check.
Overall, we present a deep-learning-based method that reliably segments most of the over
one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter
quickly enough to be used in interactive software. Our algorithm has been included in our
groups virtual reality medical image visualisation software SpectoVR with the plan to be used
as one of the puzzle piece in surgical planning and navigation, as well as in the education of
future doctors
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
Spatial frequency domain imaging towards improved detection of gastrointestinal cancers
Early detection and treatment of gastrointestinal cancers has been shown to drastically improve patients survival rates. However, wide population based screening for gastrointestinal cancers is not feasible due to its high cost, risk of potential complications, and time consuming nature. This thesis forms the proposal for the development of a cost-effective, minimally invasive device to return quantitative tissue information for gastrointestinal cancer detection in-vivo using spatial frequency domain imaging (SFDI). SFDI is a non-invasive imaging technique which can return close to real time maps of absorption and reduced scattering coefficients by projecting a 2D sinusoidal pattern onto a sample of interest. First a low-cost, conventional bench top system was constructed to characterise tissue mimicking phantoms. Phantoms were fabricated with specific absorption and reduced scattering coefficients, mimicking the variation in optical properties typically seen in healthy, cancerous, and pre-cancerous oesophageal tissue. The system shows accurate retrieval of absorption and reduced scattering coefficients of 19% and 11% error respectively. However, this bench top system consists of a bulky projector and is therefore not feasible for in-vivo imaging. For SFDI systems to be feasible for in-vivo imaging, they are required to be miniaturised. Many conditions must be considered when doing this such as various illumination conditions, lighting conditions and system geometries. Therefore to aid in the miniaturisation of the bench top system, an SFDI system was simulated in the open-source ray tracing software Blender, where the capability to simulate these conditions is possible. A material of tunable absorption and scattering properties was characterised such that the specific absorption and reduced scattering coefficients of the material were known. The simulated system shows capability in detecting optical properties of typical gastrointestinal conditions in an up-close, planar geometry, as well in a non-planar geometry of a tube simulating a lumen. Optical property imaging in the non-planar, tubular geometry was done with the use of a novel illumination pattern, developed for this work. Finally, using the knowledge gained from the simulation model, the bench top system was miniaturised to a 3 mm diameter prototype. The novel use of a fiber array producing the necessary interfering fringe patterns replaced the bulky projector. The system showed capability to image phantoms simulating typical gastrointestinal conditions at two wavelengths (515 and 660 nm), measuring absorption and reduced scattering coefficients with 15% and 6% accuracy in comparison to the bench top system for the fabricated phantoms. It is proposed that this system may be used for cost-effective, minimally invasive, quantitative imaging of the gastrointestinal tract in-vivo, providing enhanced contrast for difficult to detect cancers
2017 GREAT Day Program
SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
Modern meat: the next generation of meat from cells
Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community.
The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World.
The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia
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