Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

Automatic Synchronization of Multi-User Photo Galleries

In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

Clustering properties of a type-selected volume-limited sample of galaxies in the CFHTLS

(abridged) We present an investigation of the clustering of i'AB<24.5 galaxies in the redshift interval 0.2<z<1.2. Using 100,000 precise photometric redshifts in the four ultra-deep fields of the Canada-France Legacy Survey, we construct a set of volume-limited galaxy catalogues. We study the dependence of the amplitude and slope of the galaxy correlation function on absolute B-band rest-frame luminosity, redshift and best-fitting spectral type. We find: 1. The comoving correlation length for all galaxies decreases steadily from z~0.3 to z~1. 2. At all redshifts and luminosities, galaxies with redder rest-frame colours have clustering amplitudes between two and three times higher than bluer ones. 3. For bright red and blue galaxies, the clustering amplitude is invariant with redshift. 4. At z~0.5, less luminous galaxies have higher clustering amplitudes of around 6 h-1 Mpc. 5. The relative bias between galaxies with red and blue rest-frame colours increases gradually towards fainter absolute magnitudes. One of the principal implications of these results is that although the full galaxy population traces the underlying dark matter distribution quite well (and is therefore quite weakly biased), redder, older galaxies have clustering lengths which are almost invariant with redshift, and by z~1 are quite strongly biased.Comment: 16 pages, 18 figures, accepted for publication in Astronomy and Astrophysic