Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

Is bank debt special for the transmission of monetary policy? Evidence from the stock market

We combine existing balance sheet and stock market data with two new datasets to studywhether, how much, and why bank lending to firms matters for the transmission of monetarypolicy. The first new dataset enables us to quantify the bank dependence of firms precisely,as the ratio of bank debt to total assets. We show that a two standard deviation increase inthe bank dependence of a firm makes its stock price about 25% more responsive to monetarypolicy shocks. We explore the channels through which this effect occurs, and find that thestock prices of bank-dependent firms that borrow from financially weaker banks display astronger sensitivity to monetary policy shocks. This finding is consistent with the banklending channel, a theory according to which the strength of bank balance sheets mattersfor monetary policy transmission. We construct a new database of hedging activities andshow that the stock prices of bank-dependent firms that hedge against interest rate riskdisplay a lower sensitivity to monetary policy shocks. This finding is consistent with aninterest rate pass-through channel that operates via the direct transmission of policy ratesto lending rates associated with the widespread use of floating-rates in bank loans and creditline agreements.

Improving electricity market price scenarios by means of forecasting factor models

In liberalized electricity markets, generation Companies must build an hourly bidthat is sent to the market operator. The price at which the energy will be paid is unknown during the bidding process and has to be forecast. In this work we apply forecasting factor models to this framework and study its suitability.

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

Understanding and Fighting the Medicine Counterfeit Market

Medicine counterfeiting is a serious worldwide issue, involving networks of manufacture and distribution that are an integral part of industrialized organized crime. Despite the potentially devastating health repercussions involved, legal sanctions are often inappropriate or simply not applied. The difficulty in agreeing on a definition of counterfeiting, the huge profits made by the counterfeiters and the complexity of the market are the other main reasons for the extent of the phenomenon. Above all, international cooperation is needed to thwart the spread of counterfeiting. Moreover effort is urgently required on the legal, enforcement and scientific levels. Pharmaceutical companies and agencies have developed measures to protect the medicines and allow fast and reliable analysis of the suspect products. Several means, essentially based on chromatography and spectroscopy, are now at the disposal of the analysts to enable the distinction between genuine and counterfeit products. However the determination of the components and the use of analytical data for forensic purposes still constitute a challenge. The aim of this review article is therefore to point out the intricacy of medicine counterfeiting so that a better understanding can provide solutions to fight more efficiently against it.

How market access shapes human capital investment in a peripheral country

Human capital endowment is one of the main factors influencing the level of development of a region. This paper analyses whether remoteness from economic activity has a negative effect on human capital accumulation and, consequently, on economic development. Making use of microdata this research proves that remoteness from economic activity has contributed to explain the divergences in the level of education observed across Spanish provinces over the last 50 years. The effect is significant even when controlling for the improvement of education supply. Nonetheless, the accessibility effect has been petering out since the 1960s due to the decreasing barriers to mobility.

A study of the labour market trajectories in the Grand Duchy of Luxembourg

We present an analysis of the register of all unemployment episodes in the Grand Duchy of Luxembourg over a recent period of 55 months. We apply propensity score matching to account forthe systematic differences among the groups of subjects (registrants) and unemployment spells.We devise graphical and tabular summaries for describing the sequences of employment states ofthe members of the labour force who register at Agence pour le d?veloppement de l'emploi, theLuxembourg Public Unemployment Agency. Some employment-related information about themis collected by linking their records to the national register of social security contributions, maintained by Inspection g?n?rale de la s?curit? sociale. A class of univariate indices for characterisingthe sequences of labour force states is defined.

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Segmenting ultrasound images is a challenging problemwhere standard unsupervised segmentation methods such asthe well-known Chan-Vese method fail. We propose in thispaper an efficient segmentation method for this class ofimages. Our proposed algorithm is based on asemi-supervised approach (user labels) and the use ofimage patches as data features. We also consider thePearson distance between patches, which has been shown tobe robust w.r.t speckle noise present in ultrasoundimages. Our results on phantom and clinical data show avery high similarity agreement with the ground truthprovided by a medical expert.

Brain Surface Segmentation of Magnetic Resonance Images of the Fetus

In this work we present a method for the image analysisof Magnetic Resonance Imaging (MRI) of fetuses. Our goalis to segment the brain surface from multiple volumes(axial, coronal and sagittal acquisitions) of a fetus. Tothis end we propose a two-step approach: first, a FiniteGaussian Mixture Model (FGMM) will segment the image into3 classes: brain, non-brain and mixture voxels. Second, aMarkov Random Field scheme will be applied tore-distribute mixture voxels into either brain ornon-brain tissue. Our main contributions are an adaptedenergy computation and an extended neighborhood frommultiple volumes in the MRF step. Preliminary results onfour fetuses of different gestational ages will be shown.