Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in Biomedical Image Analysis. MICCAI 2017. Quebec Cit

Mean Field Network based Graph Refinement with application to Airway Tree Extraction

We present tree extraction in 3D images as a graph refinement task, of obtaining a subgraph from an over-complete input graph. To this end, we formulate an approximate Bayesian inference framework on undirected graphs using mean field approximation (MFA). Mean field networks are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters from training data using back-propagation algorithm. We demonstrate usefulness of the model to extract airway trees from 3D chest CT data. We first obtain probability images using a voxel classifier that distinguishes airways from background and use Bayesian smoothing to model individual airway branches. This yields us joint Gaussian density estimates of position, orientation and scale as node features of the input graph. Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images. Using centerline distance as error measure the presented method shows significant improvement compared to these two methods.Comment: 10 pages. Preprin

Bias in Estimating Multivariate and Univariate Diffusions

Published in Journal of Econometrics, 2011, https://doi.org/10.1016/j.jeconom.2010.12.006</p

Stochastic Simulation of Process Calculi for Biology

Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

Treatment of doxorubicin resistant MCF7/Dx cells with nitric oxide causes histone glutathionylation and reversal of drug resistance.

Acquired drug resistance was found to be suppressed in the doxorubicin-resistant breast cancer cell line MCF7/Dx after pre-treatment with GSNO (nitrosoglutathione). The effect was accompanied by enhanced protein glutathionylation and accumulation of doxorubicin in the nucleus. Among the glutathionylated proteins, we identified three members of the histone family; this is, to our knowledge, the first time that histone glutathionylation has been reported. Formation of the potential NO donor dinitrosyl–diglutathionyl–iron complex, bound to GSTP1-1 (glutathione transferase P1-1), was observed in both MCF7/Dx cells and drug-sensitive MCF7 cells to a similar extent. In contrast, histone glutathionylation was found to be markedly increased in the resistant MCF7/Dx cells, which also showed a 14-fold higher amount of GSTP1-1 and increased glutathione concentration compared with MCF7 cells. These results suggest that the increased cytotoxic effect of combined doxorubicin and GSNO treatment involves the glutathionylation of histones through a mechanism that requires high glutathione levels and increased expression of GSTP1-1. Owing to the critical role of histones in the regulation of gene expression, the implication of this finding may go beyond the phenomenon of doxorubicin resistance

Estimation, prediction and interpolation for nonstationary series with the Kalman filter

Digitised version produced by the EUI Library and made available online in 2020