Giuseppe Coppini

A computer-aided diagnosis approach for emphysema recognition in chest radiography

The purpose of this work is twofold: (i) to develop a CAD system for the assessment of emphysema by digital chest radiography and (ii) to test it against CT imaging. The system is based on the analysis of the shape of lung silhouette as imaged in standard chest examination. Postero-anterior and lateral views are processed to extract the contours of the lung fields automatically. Subsequently, the shape of lung silhouettes is described by polyline approximation and the computed feature-set processed by a neural network to estimate the probability of emphysema. Images of radiographic studies from 225 patients were collected and properly annotated to build an experimental dataset named EMPH. Each patient had undergone a standard two-views chest radiography and CT for diagnostic purposes. In addition, the images (247) from JSRT dataset were used to evaluate lung segmentation in postero-anterior view. System performances were assessed by: (i) analyzing the quality of the automatic segmentation of the lung silhouette against manual tracing and (ii) measuring the capabilities of emphysema recognition. As to step i, on JSRT dataset, we obtained overlap percentage (Ω) 92.7±3.3%, Dice Similarity Coefficient (DSC) 95.5±3.7% and average contour distance (ACD) 1.73±0.87 mm. On EMPH dataset we had Ω=93.1±2.9%, DSC=96.1±3.5% and ACD=1.62±0.92 mm, for the postero-anterior view, while we had Ω=94.5±4.6%, DSC=91.0±6.3% and ACD=2.22±0.86 mm, for the lateral view. As to step ii, accuracy of emphysema recognition was 95.4%, with sensitivity and specificity 94.5% and 96.1% respectively. According to experimental results our system allows reliable and inexpensive recognition of emphysema on digital chest radiography.

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. Indeed, epicardial fat has recently been shown to correlate with cardiovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns still remain about the methods for measuring epicardial fat, its regional distribution on the myocardium and the accuracy and reproducibility of the measurements. In this paper, a method is proposed for the analysis of single-frame 3D images obtained by the standard acquisition protocol used for coronary calcium scoring. In the design of the method, much attention has been payed to the minimization of user intervention and to reproducibility issues. In particular, the proposed method features a two step segmentation algorithm suitable for the analysis of epicardial fat. In the first step of the algorithm, an analysis of epicardial fat intensity distribution is carried out in order to define suitable thresholds for a first rough segmentation. In the second step, a variational formulation of level set methods - including a specially-designed region homogeneity energy based on Gaussian mixture models- is used to recover spatial coherence and smoothness of fat depots. Experimental results show that the introduced method may be efficiently used for the quantification of epicardial fat.

Mirror mirror on the wall… An intelligent multisensory mirror for well-being self-assessment

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

Computer-aided recognition of emphysema on digital chest radiography

BACKGROUNDnComputed tomography (CT) is the benchmark for diagnosis emphysema, but is costly and imparts a substantial radiation burden to the patient.nnnOBJECTIVEnTo develop a computer-aided procedure that allows recognition of emphysema on digital chest radiography by using simple descriptors of the lung shape. The procedure was tested against CT.nnnMETHODSnPatients (N=225), who had undergone postero-anterior and lateral digital chest radiographs and CT for diagnostic purposes, were studied and divided in a derivation (N=118) and in a validation sample (N=107). CT images were scored for emphysema using the picture-grading method. Simple descriptors that measure the bending characteristics of the lung profile on chest radiography were automatically extracted from the derivation sample, and applied to train a neural network to assign a probability of emphysema between 0 and 1. The diagnostic performance of the procedure was described by the area under the receiver operating characteristic curve (AUC).nnnRESULTSnAUC was 0.985 (95% confidence interval, 0.965-0.998) in the derivation sample, and 0.975 (95% confidence interval, 0.936-0.998) in the validation sample. At a probability cutpoint of 0.55, the procedure yielded 92% sensitivity and 96% specificity in the derivation sample; 90% sensitivity and 97% specificity in the validation sample. False negatives on chest radiography had trace or mild emphysema on CT.nnnCONCLUSIONSnThe computer-aided procedure is simple and inexpensive, and permits quick recognition of emphysema on digital chest radiographs.

Mirror Mirror on the Wall... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization

A persons 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 three-dimensional 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.

A virtual individual's model based on facial expression analysis: A non-intrusive approach for wellbeing monitoring and self-management

Facial expressions are visible signs of the affective and psychological state of a person, which is strictly correlated with the pathogenesis of clinically relevant diseases and more in general with individuals wellbeing. The main idea highlighted in this paper is the exploitation of the facial expression analysis for wellbeing monitoring and self-management. This will occur by an innovative multisensory device that will be able to collect images and signals, extract quantitative features of facial expression related to stress, anxiety and fatigue and map them to computational descriptors of an individuals wellbeing. The latter phase will be based on a virtual individuals model conceived to allow the computation and tracing of the daily evolution of individuals wellness. Personalized advices and coaching messages will support the user in keeping a healthy lifestyle and counteract potentially harmful behaviours. The work is part of the FP7 STREP SEMEOTICONS project whose application field will be the prevention of cardio-metabolic risk, for which healthcare systems are registering an exponential growth of social costs.

Self-monitoring systems for personalised health-care and lifestyle surveillance

The quality of life and individual well-being are universally recognised as key factors in disease prevention. In particular, lifestyle interventions are effective tools for reducing the risk and incidence of major illnesses, such as cardiovascular diseases and metabolic disorders. On the other hand, patient role is progressively shifting from being a passive recipient of care towards being a co-producer of her/his health. In this frame, novel devices and systems able to help individuals in self-evaluation are expected to play a crucial role. In this special issue we focus on innovative methodologies and technologies devoted to individual self-assessment, oriented both to healthy people to maintain their well-being, and to diseased persons to improve their care.

Computer Vision for Ambient Assisted Living

Abstract The Ambient Assisted Living (AAL) paradigm proposes advanced technologies and services to improve the quality of life, health, and wellbeing of citizens by making their daily-life activities easier and more secure, by monitoring patients under specific treatment, and by addressing at-risk subjects with proper counseling. The challenges brought by AAL range from robust, accurate, and nonintrusive data acquisition in daily-life settings to the development of services that are easy to use and appealing to the users and that support long-term engagement. This chapter offers a brief survey of existing vision-based monitoring solutions for personalized healthcare and wellness, and introduces the Wize Mirror, a multisensory platform featuring advanced algorithms for cardiometabolic risk prevention and quality-of-life improvement.

The relationship between forearm skin speed-resolved perfusion and oxygen saturation, and finger arterial pulsation amplitudes, as indirect measures of endothelial function

Endothelial function is important for regulating peripheral blood flow to meet varying metabolic demands and can be measured indirectly during vascular provocations. In this study, we compared the PAT finger response (EndoPAT) after a 5‐minutes arterial occlusion to that from forearm skin comprehensive microcirculation analysis (EPOS).

User acceptance of self-monitoring technology to prevent cardio-metabolic diseases: The Wize Mirror

Cardiovascular diseases are the leading cause of mortality worldwide and impose a tremendous burden on socio-economic costs. At present, prevention strategies based on personalized lifestyle interventions are considered the best way to contain the epidemics of these diseases. In this view, availability of innovative technological systems able to assist people in self-monitoring and self-coaching is expected to play a crucial role. Aiming to this, we have developed a multi-sensing device, called Wize Mirror which has the appearance of a conventional mirror. The Wize Mirror allows the users to self-monitor their cardio-metabolic risk also providing personalized lifestyle guidance. The mirror has undergone a validation study in three centers to assess the coherence of mirror measurements with standard clinical tests and to evaluate its acceptability by potential users. In this paper, following a summary of the major functionalities of the mirror, we report on the analysis of data about system acceptability assessed during the validation study. Acceptability was measured by means of the System Usability Scale, which resulted in “good” usability of the Wize Mirror.