Elisa Veronese

Heterogeneity of Cortical Lesions in Multiple Sclerosis: An MRI Perfusion Study

In this study, dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) was used to quantify the cerebral blood flow (CBF), the cerebral blood volume (CBV), and the mean transit time (MTT) and to analyze the changes in cerebral perfusion associated with the cortical lesions in 44 patients with relapsing-remitting multiple sclerosis. The cortical lesions showed a statistically significant reduction in CBF and CBV compared with the normal-appearing gray matter, whereas there were no significant changes in the MTT. The reduced perfusion suggests a reduction of metabolism because of the loss of cortical neurons. A small population of outliers showing an increased CBF and/or CBV has also been detected. The presence of hyperperfused outliers may imply that perfusion could evolve during inflammation. These findings show that perfusion is altered in cortical lesions and that DSC-MRI can be a useful tool to investigate more deeply the evolution of cortical lesions in multiple sclerosis.

Machine Learning Approaches: From Theory to Application in Schizophrenia

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

Developmental Programming of Cardiovascular Risk in Intrauterine Growth-Restricted Twin Fetuses According to Aortic Intima Thickness

We aimed to test the hypothesis that aortic intima thickness is greater in intrauterine growth‐restricted (IUGR) twin fetuses compared to normally developing twins, thus defining an increased cardiovascular risk that reflects genetic factors in fetuses sharing the same womb.

Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours

Rheumatoid arthritis (RA) is a chronic multisystemic autoimmune disease, with an unclear etiopathogenesis. Its early diagnosis and activity assessment are essential to adjust the proper therapy. Among the different imaging techniques, ultrasonography (US) allows direct visualization of early inflammatory joint changes as synovitis, being also rapidly performed and easily accepted by patients. We propose an algorithm to semi-automatically detect synovial boundaries on US images, requiring minimal user interaction. In order to identify the synovia-bone and the synovia-soft tissues interfaces, and to tackle the morphological variability of diseased joints, a cascade of two different active contours is developed, whose composition corresponds to the whole synovial boundary. The algorithm was tested on US images acquired from proximal interphalangeal (PIP) and metacarpophalangeal (MCP) finger joints of 34 subjects. The results have been compared with a consensus manual segmentation. We obtained an overall mean sensitivity of 85±13%, and a mean Dices similarity index of 80±8%, with a mean Hausdorff distance from the manual segmentation of 28±10 pixels (approximately 1.4±0.5mm), that are a better performance than those obtained by the raters with respect to the consensus.

Estimation of prenatal aorta intima-media thickness from ultrasound examination

Prenatal events such as intrauterine growth restriction and increased cardiovascular risk in later life have been shown to be associated with an increased intima-media thickness (aIMT) of the abdominal aorta in the fetus. In order to assess and manage atherosclerosis and cardiovascular disease risk in adults and children, in recent years the measurement of abdominal and carotid artery thickness has gained a growing appeal. Nevertheless, no computer aided method has been proposed for the analysis of prenatal vessels from ultrasound data, yet. To date, these measurements are being performed manually on ultrasound fetal images by skilled practitioners. The aim of the presented study is to introduce an automatic algorithm that identifies abdominal aorta and estimates its diameter and aIMT from routine third trimester ultrasonographic fetal data.The algorithm locates the aorta, then segments it and, by modeling the arterial wall longitudinal sections by means of a gaussian mixture, derives a set of measures of the aorta diameter (aDiam) and of the intima-media thickness (aIMT). After estimating the cardiac cycle, the mean diameter and the aIMT at the end-diastole phase are computed.Considering the aIMT value for each subject, the correlation between automatic and manual end-diastolic aIMT measurements is 0.91 in a range of values 0.44-1.10 mm, corresponding to both normal and pathological conditions. The automatic system yields a mean relative error of 19%, that is similar to the intra-observer variability (14%) and much lower that the inter-observer variability (42%).The correlation between manual and automatic measurements and the small error confirm the ability of the proposed system to reliably estimate aIMT values in prenatal ultrasound sequences, reducing measurement variability and suggesting that it can be used for an automatic assessment of aIMT.

Hybrid patch-based and image-wide classification of confocal laser endomicroscopy images in Barrett's esophagus surveillance

Barretts esophagus (BE) is a premalignant condition characterized by the replacement of normal squamous esophageal epithelium by metaplastic intestinal epithelium containing goblet cells. To be diagnosed and monitored, BE requires a thorough observation of epithelial macro- and microscopic changes. Confocal laser endomicroscopy (CLE) has recently revealed to be a useful technique for in vivo virtual histology for BE surveillance. We present a computer-based method for the automatic classification of gastric metaplasia (GM), intestinal metaplasia (IM) and neoplasia (NPL) on the basis of appearance features of confocal images. Comparing the automatic results with the histological gold standard, the proposed method classifies IM, GM, and NPL confocal images with accuracy comparable to human observer. Moreover, it increases the sensitivity and the specificity of CLE examinations, thus decreasing the number of biopsies needed for BE and neoplasia diagnosis.

Estimation of prenatal aorta intima-media thickness in ultrasound examination

Prenatal events such as intrauterine growth restriction have been shown to be associated with an increased thickness of abdominal aorta in the fetus. Therefore the measurement of abdominal aortic intima-media thickness (aIMT) has been recently considered a sensitive marker of artherosclerosis risk. To date measure of aortic diameter and of aIMT has been performed manually on US fetal images, thus being susceptible to intra- and inter- operator variability. This work introduces an automatic algorithm that identifies abdominal aorta and estimates its diameter and aIMT from videos recorded during routine third trimester ultrasonographic fetal biometry. Firstly, in each frame, the algorithm locates and segments the region corresponding to aorta by means of an active contour driven by two different external forces: a static vector field convolution force and a dynamic pressure force. Then, in each frame, the mean diameter of the vessel is computed, to reconstruct the cardiac cycle: in fact, we expect the diameter to have a sinusoidal trend, according to the heart rate. From the obtained sinusoid, we identify the frames corresponding to the end diastole and to the end systole. Finally, in these frames we assess the aIMT. According to its definition, we consider as aIMT the distance between the leading edge of the blood-intima interface, and the leading edge of the media-adventitia interface on the far wall of the vessel. The correlation between end-diastole and end-systole aIMT automatic and manual measures is 0.90 and 0.84 respectively.

Automatic Segmentation of Gray Matter Multiple Sclerosis Lesions on DIR Images

Multiple Sclerosis (MS) is a chronic inflammatory-demyelinating disease that affects both white and gray matter (GM). GM lesions have been demonstrated to play a major role in the physical and cognitive disability and in the disease progression. The diagnosis and monitoring of the disease is mainly based on magnetic resonance imaging (MRI). Lesions identification needs visual detection performed by experienced graders, a process that is always time consuming, error prone and operator dependent.We present a technique to automatically estimate GM lesion load from double inversion recovery (DIR) MRI sequences. We tested the proposed algorithm on DIR sequences acquired from 50 MS patients. Regions corresponding to probable GM lesions were manually labeled to provide a reference. The resulting automatic lesion load estimate provides a correlation of 98.5% with manual lesion number, and of 99.3% with manual lesion volume.

Semiautomatic Evaluation of Crypt Architecture and Vessel Morphology in Confocal Microendoscopy: Application to Ulcerative Colitis

Neoangiogenesis plays a central role in both the initiation and perpetuation of the inflammatory response during chronic intestinal inflammation. However, limited data is available on the microvascular and crypt architecture during remission phases. In this study we have evaluated the intestinal mucosa of UC patients in clinical and endoscopic remission by probe-based confocal endomicroscopy (p-CLE) and quantified microvessel tortuosity and crypt architecture by a semiautomated analysis. The system firstly automatically identifies the crypts visible in the images, and estimates the graph connecting their centers in order to estimate the crypt architecture. Hyperfluorescence in the peri-crypt region is evaluated, and leakge is automatically detected. Length and tortuosity of the manually inserted vessels assess the vascular state. 6 patients with active UC and 2 with inactive UC were evaluated acquiring 19 set of p-CLE images with matched biopsies, showing significant differences in crypt architecture, vessel morphology and hyperfluorescence patterns.

Quantitative Assessment of Prenatal AorticWall Thickness in Gestational Diabetes

Intrauterine environment, and especially a mismatch between the early and later-life environments, is thought to induce epigenetic and morphological changes that may manifest in later life as an increased vulnerability to non communicable diseases as diabetes. Prenatal events, such as intrauterine growth restriction, as well as an increased risk of developing diabetes and cardiovascular alterations, have been shown to be associated with an increased intima-media thickness (aIMT) of the abdominal aorta in the fetus. To date its measure, has been performed manually on ultrasound fetal images by skilled practitioners.We present an automatic algorithm that identifies abdominal aorta and estimates its diameter and thickness from routine third trimester ultrasonographic fetal data, providing a correlation between end-diastole aIMT automatic and manual measures of 0.96, with a mean error of 0.02 mm, and a relative error of 3%.