An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications

Sketching sound with voice and gesture

Voice and gestures are natural sketching tools that can be exploited to communicate sonic interactions. In product and interaction design, sounds should be included in the early stages of the design process. Scientists of human motion have shown that auditory stimuli are important in the performance of difficult tasks and can elicit anticipatory postural adjustments in athletes. These findings justify the attention given to sound in interaction design for gaming, especially in action and sports games that afford the development of levels of virtuosity. The sonic manifestations of objects can be designed by acting on their mechanical qualities and by augmenting the objects with synthetic and responsive sound

The VITROVAC Cavity for the TERA/PIMMS Medical Synchrotron

A proton and light-ion medical synchrotron is characterised by a large frequency swing for the RF between the injection and the top energy. For this purpose, a VITROVAC®-loaded RF cavity has been developed for the Proton-Ion Medical Machine Study (PIMMS) at CERN, and for TERA, the Italian project of a proton and light-ion synchrotron for cancer therapy, based on the PIMMS study. The main features are a large frequency swing, particularly extended to the low frequency range, a very large relative permeability and a low Q factor. The total power needed is less than 100 kW, while a very small bias power is required for the frequency tuning. The main mechanical characteristics are compactness (less than 1.5 m), and simplicity of construction. As a result, the requirements of the medical synchrotron are comfortably satisfied, namely: 0.4 to 3 MHz swing, 3 kV peak voltage at a repetition rate of less than 1 s

DEVELOPMENT OF A PCR-RFLP METOD FOR THE IDENTIFICATION OF SIX SPECIES BELONGING TO THE GENUS LOPHIUS

Nowadays, six of the seven species belonging to the Genus Lophius have an important commercial value in the national and international markets. Usually they are sold beheaded and for this reason they are called tails. This kind of preparation is a limit for the specie-identification by means of the morphological characteristics. The mitochondrial cytochrome b (Cyt b) gene is considered a useful genetic marker to identify fish species. In this work, after obtaining the Cyt b complete sequence of the Lophius species that were missed in the databases, we set up a method based on PCR-RFLP able to identify the six species of Lophius with a commercial denomination in the Italian market

Vocal imitations and the identification of sound events

International audienceIt is commonly observed that a speaker vocally imitates a sound that she or he intends to communicate to an interlocutor. We report on an experiment that examined the assumption that vocal imitations can e ffectively communicate a referent sound, and that they do so by conveying the features necessary for the identifi cation of the referent sound event. Subjects were required to sort a set of vocal imitations of everyday sounds. The resulting clusters corresponded in most of the cases to the categories of the referent sound events, indicating that the imitations enabled the listeners to recover what was imitated. Furthermore, a binary decision tree analysis showed that a few characteristic acoustic features predicted the clusters. These features also predicted the classi fication of the referent sounds, but did not generalize to the categorization of other sounds. This showed that, for the speaker, vocally imitating a sound consists of conveying the acoustic features important for recognition, within the constraints of human vocal production. As such vocal imitations prove to be a phenomenon potentially useful to study sound identifi cation

What determines auditory similarity? The effect of stimulus group and methodology.

Two experiments on the internal representation of auditory stimuli compared the pairwise and grouping methodologies as means of deriving similarity judgements. A total of 45 undergraduate students participated in each experiment, judging the similarity of short auditory stimuli, using one of the methodologies. The experiments support and extend Bonebright's (1996) findings, using a further 60 stimuli. Results from both methodologies highlight the importance of category information and acoustic features, such as root mean square (RMS) power and pitch, in similarity judgements. Results showed that the grouping task is a viable alternative to the pairwise task with N > 20 sounds whilst highlighting subtle differences, such as cluster tightness, between the different task results. The grouping task is more likely to yield category information as underlying similarity judgements