Imaging in clinical oncology trials provides a wealth of information that contributes to the drug development process, especially in early phase studies. This article focuses on kinetic modeling in DCE-MRI, inspired by mixed-effects models that are frequently used in the analysis of clinical trials. Instead of summarizing each scanning session as a single kinetic parameter--such as median k(tra...

In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is an edge-preserving smoothness prior which is imposed on the probabilities of the voxel labels. This prior incorporates a line process, which is modeled as a Bernoulli random variable, in order to preserve edges between tissues. The main difference with other, st...

Decoding of perceptual and mental states using multivariate analysis methods has received much recent attention. It relies on selective responses to experimental conditions in single trials, aggregated across voxels. In this study, we show that decoding is also possible when the state of interest changes continuously over time. It is shown that both orientation and rotation direction of a conti...

Machine Learning techniques have been used quite widely for the task of predicting cognitive processes from fMRI data. However, these models do not describe well the fMRI signal when it is generated by multiple cognitive processes that are simultaneously active. In this paper we consider the problem of accurately modeling the fMRI signal of a human subject who is performing a task involving mul...

The ability to extract properties from 3D geological framework models for use in the construction of conceptual and mathematical models is seen as increasingly important, however, tools and techniques are needed to support such information flows. Developing such methodologies will maximize the opportunity for information use and re-use, this is particularly important as the true value of such a...

Imaging in clinical oncology trials provides a wealth of information that contributes to the drug development process, especially in early phase studies. This paper focuses on kinetic modeling in DCE-MRI, inspired by mixed-effects models that are frequently used in the analysis of clinical trials. Instead of summarizing each scanning session as a single kinetic parameter – such as median K acro...

The autonomous car must recognize the driving environment quickly for safe driving. As Light Detection And Range (LiDAR) sensor is widely used in car, fast semantic segmentation of LiDAR point cloud, which point-wise classification cloud within framerate, has attracted attention recognition environment. Although voxel and fusion-based models are state-of-the-art model recently, their real-time ...

A 3-D Monte Carlo ray-tracing simulation of LiDAR propagation models the reflection, transmission and absorption interactions of laser energy with materials in a simulated scene. In this presentation, a model scene consisting of a single Victorian Boxwood (Pittosporum undulatum) tree is generated by the high-fidelity tree voxel model VoxLAD using high-spatial resolution point cloud data from a ...

Recent advances in technology have paved the way for humanagent interactions to become ubiquitous in our daily lives, and decades worth of research on virtual agents have enhanced these interactions. However, for the most part, the effect of different types of agents on the human brain is unknown, and the neuroscience of human-agent interactions is rarely studied. In this study, we examine the ...

We describe the application of an active appearance model (AAM) based method to segmentation of the caudate nuclei. A “composite” 3D profile AAM was constructed from the surfaces of 15 subcortical structures using a training set of 50 subjects, and individual AAMs of the left and right caudate constructed from 227 subjects. Segmentation starts with affine registration to initialise the composit...