Quantification of Epicardial Fat by Cardiac CT Imaging

The aim of this work is to introduce and design image processing methods for the quantitative analysis of epicardial fat by using cardiac CT imaging

Mirror mirror on the wall... an unobtrusive intelligent multisensory mirror for well-being status self-assessment and visualization

A person’s well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos and 3D scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data

Assessment of advanced glycated end product accumulation in skin using auto fluorescence multispectral imaging

Several studies have shown that advanced glycation end products (AGE) play a role in both the microvascular and macrovascular complications of diabetes and are closely linked to inflammation and atherosclerosis. AGEs accumulate in skin and can be detected using their auto fluorescence (AF).A significant correlation exists between AGE AF and the levels of AGEs as obtained from skin biopsies. A commercial device, the AGE Reader, has become available to assess skin AF for clinical purposes but, while displaying promising results, it is limited to single-point measurements performed in contact to skin tissue. Furthermore, in vivo imaging of AGE accumulation is virtually unexplored.We proposed a non-invasive, contact-less novel technique for quantifying fluorescent AGE deposits in skin tissue using a multispectral imaging camera setup (MSI) during ultraviolet (UV) exposure. Imaging involved applying a region-of-interest mask, avoiding specular reflections and a simple calibration. Results of a study conducted on 16 subjects with skin types ranging from fair to deeply pigmented skin, showed that AGE measured with MSI in forearm skin was significantly correlated with the AGE reference method (AGE Reader on forearm skin, R=0.68, p=0.005). AGE measured in facial skin was borderline significantly related to AGE Reader on forearm skin (R=0.47, p=0.078). These results support the use of the technique in devices for non-touch measurement of AGE content in either facial or forearm skin tissue over time.SEMEOTICON

Recognition of Stress Activation by Unobtrusive Multi Sensing Setup

It is recognized that stress conditions play an important role in the definition of individual wellness and represent a major risk factor for most non-communicable diseases. Most studies focus on the evaluation of response to maximal stress conditions while a few of them reports results about the detection/monitoring of response to mild stimulations. In this study, we investigate the capability of some physiological signs and indicators (including Heart Rate, Heart Rate Variability, Respiratory Rate, Galvanic Skin Response) to recognize stress in response to moderate cognitive activation in daily life settings. To achieve this goal, we built up an unobtrusive platform to collect signals from healthy volunteers (10 subjects) undergoing cognitive activation via Stroop Color Word Test. We integrated our dataset with data from the Stress Recognition in the Automobile Drivers dataset. Following data harmonization, signal recordings in both datasets were split into five-minute blocks and a set of 12 features was extracted from each block. A feature selection was implemented by two complementary approaches: Sequential Forward Feature Selection (SFFS) and Auto-Encoder (AE) neural networks. Finally, we explored the use of Self-Organizing Map (SOM) to provide a flexible representation of an individual status. From the initial feature set we have determined, by SFFS analysis, that 2 of them (median Respiratory Rate and number peaks in Galvanic Skin Response signals) can discriminate activation statuses from resting ones. In addition, AE experiments also support that two features can suffice for recognition. Finally, we showed that SOM can provide a comprehensive but compact description of activation statuses allowing a fine prototypical representation of individual status

Morpho-functional imaging of coronary anatomy and left ventricular perfusion obtained by cardiac CT

Volumetric computed tomography (CT) angiography has become a standard non-invasive routine procedure for cardiac imaging and coronary arteries pathology detection. However, before the diagnosis process, a pre-processing task is critical for an accurate examination of the vessels. Specially, the user has to manually remove obscuring structures in order to get an accurate visualization of coronary arteries. Indeed, the coronaries are always hidden by surrounding organs of the heart such as liver, sternum, ribs and lungs which prevent the pathologist from getting a clear view of the heart surface. In this paper, we propose a fast algorithm to automatically isolate the heart anatomy in 3D CT cardiac data sets. Our work eliminates the tedious and time consuming step of the manual delineation and pro- vides a clear and well defined view of the coronary arteries. Consequently, the user can quickly identify suspicious segments on the isolated heart. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization [1]. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object

Epicardial fat volume assessment in cardiac CT

Epicardial fat, as other visceral fat localizations, is correlated with car- diovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns remain about the method for measuring epi- cardial fat, its regional distribution on the myocardium, as well as the accuracy and reproducibility of such measurements. At present, dedi- cated software procedures to assess epicardial fat are lacking. On the other hand, manual fat segmentation requires a huge and tedious operator intervention, which is expected to cause inaccuracy and large observer- dependent variability. The aim of this study was twofold: (1) the devel- opment of a procedure devoted to assess the volume of epicardial fat, (2) the evaluation of the related intra and inter-observer variability in CT scans, both with and without contrast medium injection