https://surfer.nmr.mgh.harvard.edu/ftp/articles/desikan06-parcellation.pdf

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

The minimal preprocessing pipelines for the Human Connectome Project.

https://bic.berkeley.edu/sites/default/files/Rossi_09_TMS%20safety%20review.pdf

Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research

In this article, we review probabilistic topic models: graphical models that can be used to summarize a large collection of documents with a smaller number of distributions over words. Those distributions are called "topics" because, when fit to data, they capture the salient themes that run through the collection. We describe both finite-dimensional parametric topic models and their Bayesian nonparametric counterparts, which are based on the hierarchical Dirichlet process (HDP). We discuss two extensions of topic models to time-series data-one that lets the topics slowly change over time and one that lets the assumed prevalence of the topics change. Finally, we illustrate the application of topic models to nontext data, summarizing some recent research results in image analysis.

/pdf/probabilistic-topic-models-4cq6hpiogr.pdf

Probabilistic Topic Models

http://ee.sharif.edu/~miap/Files/A%20Survey%20of%20Medical%20Image%20Registration.pdf

A survey of medical image registration.

In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, sources and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.

/pdf/learning-semantic-scene-models-by-trajectory-analysis-2c4eq8eflj.pdf

Learning semantic scene models by trajectory analysis

Mutual information in coupled multi-shape model for medical image segmentation

The registration of pre-operative volumetric datasets to intra-operative two-dimensional images provides an improved way of verifying patient position and medical instrument location. In applications from orthopedics to neurosurgery, it has great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information-based registration algorithm which establishes the proper alignment via a stochastic gradient ascent strategy. Our main contribution lies in estimating probability density measures of image intensities with a sparse histogramming method which could lead to potential speedup over existing registration procedures and deriving the gradient estimates required by the maximization procedure. Experimental results are presented on fluoroscopy and CT datasets of a real skull, and on a CT-derived dataset of a real skull, a plastic skull and a plastic lumbar spine segment.

/pdf/2d-3d-rigid-registration-of-x-ray-fluoroscopy-and-ct-images-2ad4pnbnxm.pdf

2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators

We propose a technique to accurately correct the spherical topology of cortical surfaces. We construct a mapping from the original surface onto the sphere to detect topological defects as minimal non-homeomorphic regions. A genetic algorithm corrects each defect by finding the maximum-a-posteriori retessellation in a Bayesian framework. During the genetic search, incorrect vertices are iteratively identified and eliminated, while the optimal retessellation is constructed. Applied to synthetic and real data, our method generates optimal topological corrections with only a few iterations.

/pdf/a-genetic-algorithm-for-the-topology-correction-of-cortical-d21tt5bddd.pdf

A genetic algorithm for the topology correction of cortical surfaces

We present a novel method for constructing dependent Dirichlet processes. The approach exploits the intrinsic relationship between Dirichlet and Poisson processes in order to create a Markov chain of Dirichlet processes suitable for use as a prior over evolving mixture models. The method allows for the creation, removal, and location variation of component models over time while maintaining the property that the random measures are marginally DP distributed. Additionally, we derive a Gibbs sampling algorithm for model inference and test it on both synthetic and real data. Empirical results demonstrate that the approach is effective in estimating dynamically varying mixture models.

/pdf/construction-of-dependent-dirichlet-processes-based-on-18ervaamwq.pdf

Construction of Dependent Dirichlet Processes based on Poisson Processes

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