Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.

How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index.

Cortical folding (gyrification) is determined during the first months of life, so that adverse events occurring during this period leave traces that will be identifiable at any age. As recently reviewed by Mangin and colleagues(2), several methods exist to quantify different characteristics of gyrification. For instance, sulcal morphometry can be used to measure shape descriptors such as the depth, length or indices of inter-hemispheric asymmetry(3). These geometrical properties have the advantage of being easy to interpret. However, sulcal morphometry tightly relies on the accurate identification of a given set of sulci and hence provides a fragmented description of gyrification. A more fine-grained quantification of gyrification can be achieved with curvature-based measurements, where smoothed absolute mean curvature is typically computed at thousands of points over the cortical surface(4). The curvature is however not straightforward to comprehend, as it remains unclear if there is any direct relationship between the curvedness and a biologically meaningful correlate such as cortical volume or surface. To address the diverse issues raised by the measurement of cortical folding, we previously developed an algorithm to quantify local gyrification with an exquisite spatial resolution and of simple interpretation. Our method is inspired of the Gyrification Index(5), a method originally used in comparative neuroanatomy to evaluate the cortical folding differences across species. In our implementation, which we name local Gyrification Index (lGI(1)), we measure the amount of cortex buried within the sulcal folds as compared with the amount of visible cortex in circular regions of interest. Given that the cortex grows primarily through radial expansion(6), our method was specifically designed to identify early defects of cortical development. In this article, we detail the computation of local Gyrification Index, which is now freely distributed as a part of the FreeSurfer Software (http://surfer.nmr.mgh.harvard.edu/, Martinos Center for Biomedical Imaging, Massachusetts General Hospital). FreeSurfer provides a set of automated reconstruction tools of the brain's cortical surface from structural MRI data. The cortical surface extracted in the native space of the images with sub-millimeter accuracy is then further used for the creation of an outer surface, which will serve as a basis for the lGI calculation. A circular region of interest is then delineated on the outer surface, and its corresponding region of interest on the cortical surface is identified using a matching algorithm as described in our validation study(1). This process is repeatedly iterated with largely overlapping regions of interest, resulting in cortical maps of gyrification for subsequent statistical comparisons (Fig. 1). Of note, another measurement of local gyrification with a similar inspiration was proposed by Toro and colleagues(7), where the folding index at each point is computed as the ratio of the cortical area contained in a sphere divided by the area of a disc with the same radius. The two implementations differ in that the one by Toro et al. is based on Euclidian distances and thus considers discontinuous patches of cortical area, whereas ours uses a strict geodesic algorithm and include only the continuous patch of cortical area opening at the brain surface in a circular region of interest.

Evidence for a sodium-induced activation of central neurogenic mechanisms in one-kidney, one-clip renal hypertensive rats

The mechanisms sustaining high blood pressure in conscious one-kidney, one-clip Goldblatt rats were evaluated with the use of SK&F 64139, a phenylethanolamine N-methyltransferase inhibitor capable of crossing the blood-brain barrier and of captopril, an angiotensin converting enzyme inhibitor. The rats were studied 3 weeks after left renal artery clipping and contralateral nephrectomy. During the developmental phase of hypertension, two groups of rats were maintained on a regular salt (RNa) intake, whereas two other groups were given a low salt (LNa) diet. On the day of the experiment, the base-line mean blood pressure measured in the LNa rats (177.4 +/- 5.2 mm Hg, mean +/- S.E., n = 15) was similar to that measured in the RNa rats (178.7 +/- 5.4 mm Hg, n = 16). SK&F 64139 (12.5 mg p.o.) induced a significantly more pronounced (P less than .001) blood pressure decrease in the RNa rats (-25.6 +/- 3.6 mm Hg, n = 8) than in the LNa rats (-4.3 +/- 3.3 mm Hg, n = 7) during a 90-min observation period. On the other hand, captopril (10 mg p.o.) normalized blood pressure in LNa rats (n = 8), but produced only a 13.4 mm Hg blood pressure drop in RNa rats (n = 8). RNa rats treated with SK&F 64139 were found to have decreased phenylethanolamine N-methyltransferase activity by an average 80% in selected brain stem nuclei when compared with nontreated rats. No significant difference in plasma catecholamine levels was found between the RNa and LNa rats. These results suggest that, in this experimental model of hypertension, the sodium ion might increase the model of hypertension, the sodium ion might increase the vasoconstrictor contribution of the sympathetic system via a centrally mediated neurogenic mechanism while at the same time it decreases the renin-dependency of the high blood pressure.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Qualitative analysis of barriers and facilitators encountered by HIV patients in an ART adherence programme.

Background Medication adherence is a complex, dynamic and changing behaviour that is affected by a variety of factors, including the patient's beliefs and life circumstances. Studies have highlighted barriers to medication adherence (e.g., unmanaged side effects or a lack of social support), as well as facilitators of medication adherence (e.g., technical simplicity of treatment and psychological acceptance of the disease). Since August 2004, in Lausanne (Switzerland), physicians have referred patients who are either experiencing or are at risk of experiencing problems with their HIV antiretroviral treatment (ART) to a routine interdisciplinary ART adherence programme. This programme consists of multifactorial intervention including electronic drug monitoring (MEMS(TM)). Objective This study's objective was to identify the barriers and facilitators encountered by HIV patients with suboptimal medication adherence (≤90 % adherence over the study period). Setting The community pharmacy of the Department of Ambulatory Care and Community Medicine in Lausanne (Switzerland). Method The study consisted of a retrospective, qualitative, thematic content analysis of pharmacists' notes that were taken during semi-structured interviews with patients and conducted as part of the ART adherence programme between August 2004 and May 2008. Main outcome measure Barriers and facilitators encountered by HIV patients. Results Barriers to and facilitators of adherence were identified for the 17 included patients. These factors fell into three main categories: (1) cognitive, emotional and motivational; (2) environmental, organisational and social; and (3) treatment and disease. Conclusion The pharmacists' notes revealed that diverse barriers and facilitators were discussed during medication adherence interviews. Indeed, the results showed that the 17 non-adherent patients encountered barriers and benefited from facilitators. Therefore, pharmacists should inquire about all factors, regardless of whether they have a negative or a positive impact on medication adherence, and should consider all dimensions of patient adherence. The simultaneous strengthening of facilitators and better management of barriers may allow healthcare providers to tailor care to a patient's specific needs and support each individual patient in improving his medication-related behaviour.

Piercing among adolescents: body art as risk marker.

PURPOSE: To ascertain the prevalence of piercing among a nationally representative sample of adolescents; to assess whether having a piercing is a marker for risk behaviors; and to determine whether having more than one piercing is a cumulative marker for risk behaviors. METHODS: Data were drawn from a cross-sectional survey of a nationally representative sample of adolescents aged 16 to 20 years (N=7548). Controlling for background variables, pierced and non-pierced youth were compared on risk behaviors related to drug use, sexual behavior, and suicide. In a second step, adolescents having one piercing were compared with those having more than one. In both cases, statistically significant variables in the bivariate analysis were included in a logistic regression. Analyses were conducted separately by gender. RESULTS: Overall, 20.2% of our sample had a piercing (excluding earlobes), and it was significantly more prevalent among females than among males (33.8% vs. 7.4%; P<.001). In the bivariate analysis, all risk behaviors were significantly associated with having a piercing, and most of them remained significant in the multivariate analysis. One third of pierced subjects had more than one piercing, with no gender difference in prevalence. In the multivariate analysis, females with more than one piercing were more likely to have had multiple partners and to use cannabis, while no differences were noted for males. CONCLUSIONS: Body piercing is becoming popular among Swiss adolescents, especially females. Having a body piercing seems to be a risk marker for risk behaviors. Moreover, having multiple piercings is a cumulative risk marker for females.