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

Tumour growth and resistance to gemcitabine of pancreatic cancer cells are decreased by AP-2α overexpression

International audienceBACKGROUND: Activator protein-2alpha (AP-2alpha) is a transcription factor that belongs to the family of AP-2 proteins that have essential roles in tumorigenesis. Indeed, AP-2alpha is considered as a tumour-suppressor gene in different tissues such as colonic, prostatic or breast epithelial cells. Moreover, AP-2alpha also participates in the control of colon and breast cancer cells sensitivity towards chemotherapeutic drugs. Despite its potential interest, very few data are available regarding the roles of AP-2alpha in pancreatic cancer. METHODS: We have developed a stable pancreatic CAPAN-1 cell line overexpressing AP-2alpha. Consequences of overexpression were studied in terms of in vivo cell growth, gene expression, migration capacity and chemosensitivity. RESULTS: In vivo tumour growth of CAPAN-1 cells overexpressing AP-2alpha was significantly decreased by comparison to control cells. An altered expression pattern of cell cycle-controlling factors (CDK-4, CDK-6, cyclin-G1, p27(kip1) and p57(kip2)) was observed in AP-2alpha-overexpressing clones by microarrays and western blot analysis. Promoter activity and ChIP analysis indicated that AP-2alpha induces p27(kip1) protein levels by direct binding to and transactivation of its promoter. Moreover, AP-2alpha overexpression increased the chemosensitivity of CAPAN-1 cells to low doses of gemcitabine and reduced their in vitro migration capacity. CONCLUSION: Our data suggested that AP-2alpha overexpression could be exploited to decrease in vivo tumour growth of pancreatic cancer cells and to increase their sensitivity to gemcitabine

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