SubCMap: subject and condition specific effect maps

Current methods for statistical analysis of neuroimaging data identify condition related structural alterations in the human brain by detecting group differences. They construct detailed maps showing population-wide changes due to a condition of interest. Although extremely useful, methods do not provide information on the subject-specific structural alterations and they have limited diagnostic value because group assignments for each subject are required for the analysis. In this article, we propose SubCMap, a novel method to detect subject and condition specific structural alterations. SubCMap is designed to work without the group assignment information in order to provide diagnostic value. Unlike outlier detection methods, SubCMap detections are condition-specific and can be used to study the effects of various conditions or for diagnosing diseases. The method combines techniques from classification, generalization error estimation and image restoration to the identify the condition-related alterations. Experimental evaluation is performed on synthetically generated data as well as data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results on synthetic data demonstrate the advantages of SubCMap compared to population-wide techniques and higher detection accuracy compared to outlier detection. Analysis with the ADNI dataset show that SubCMap detections on cortical thickness data well correlate with non-imaging markers of Alzheimer's Disease (AD), the Mini Mental State Examination Score and Cerebrospinal Fluid amyloid-β levels, suggesting the proposed method well captures the inter-subject variation of AD effects

Medical image segmentation and analysis using statistical shape modelling and inter-landmark relationships

The study of anatomical morphology is of great importance to medical imaging, with applications varying from clinical diagnosis to computer-aided surgery. To this end, automated tools are required for accurate extraction of the anatomical boundaries from the image data and detailed interpretation of morphological information. This thesis introduces a novel approach to shape-based analysis of medical images based on Inter- Landmark Descriptors (ILDs). Unlike point coordinates that describe absolute position, these shape variables represent relative configuration of landmarks in the shape. The proposed work is motivated by the inherent difficulties of methods based on landmark coordinates in challenging applications. Through explicit invariance to pose parameters and decomposition of the global shape constraints, this work permits anatomical shape analysis that is resistant to image inhomogeneities and geometrical inconsistencies. Several algorithms are presented to tackle specific image segmentation and analysis problems, including automatic initialisation, optimal feature point search, outlier handling and dynamic abnormality localisation. Detailed validation results are provided based on various cardiovascular magnetic resonance datasets, showing increased robustness and accuracy.Open acces

Methods for event time series prediction and anomaly detection

Event time series are sequences of events occurring in continuous time. They arise in many real-world problems and may represent, for example, posts in social media, administrations of medications to patients, or adverse events, such as episodes of atrial fibrillation or earthquakes. In this work, we study and develop methods for prediction and anomaly detection on event time series. We study two general approaches. The first approach converts event time series to regular time series of counts via time discretization. We develop methods relying on (a) nonparametric time series decomposition and (b) dynamic linear models for regular time series. The second approach models the events in continuous time directly. We develop methods relying on point processes. For prediction, we develop a new model based on point processes to combine the advantages of existing models. It is flexible enough to capture complex dependency structures between events, while not sacrificing applicability in common scenarios. For anomaly detection, we develop methods that can detect new types of anomalies in continuous time and that show advantages compared to time discretization

Multivariate classification of gene expression microarray data

L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal