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Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.

Objective: To develop and implement a method for three-dimensional (3D) reconstruction of coronary arteries from conventional monoplane angiograms. Background: 3D reconstruction of conventional coronary angiograms is a promising imaging modality for both diagnostic and interventional purposes. Methods: Our method combines image enhancement, automatic edge detection, an iterative method to reconstruct the centerline of the artery and reconstruction of the diameter of the vessel by taking into consideration foreshortening effects. The X-Ray-based 3D coronary trees were compared against phantom data from a virtual arterial tree projected into two planes as well as computed tomography (CT)-based coronary artery reconstructions in patients subjected to coronary angiography. Results: Comparison against the phantom arterial tree demonstrated perfect agreement with the developed algorithm. Visual comparison against the CT-based reconstruction was performed in the 3D space, in terms of the direction angle along the centerline length of the left anterior descending and circumflex arteries relative to the main stem, and location and take-off angle of sample bifurcation branches from the main coronary arteries. Only minimal differences were detected between the two methods. Inter-and intraobserver variability of our method was low (intra-class correlation coefficients > 0.8). Conclusion: The developed method for coronary artery reconstruction from conventional angiography images provides the geometry of coronary arteries in the 3D space. '

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This study aimed at investigating the effect of myocardial motion on pulsating blood flow distribution of the left anterior descending coronary artery in the presence of atheromatous stenosis. The moving 3D arterial tree geometry has been obtained from conventional x-ray angiograms obtained during the heart cycle and includes a number of major branches. The geometry reconstruction model has been validated against projection data from a virtual phantom arterial tree as well as with CT-based reconstruction data for the same patient investigated. Reconstructions have been obtained for a number of temporal points while linear interpolation has been used for all intermediate instances. Blood has been considered as a non-Newtonian fluid. Results have been obtained using the same pulse for the inlet blood flow rate but with fixed arterial tree geometry as well as under steady-state conditions corresponding to the mean flow rate. Predictions indicate that myocardial motion has only a minor effect on flow distribution within the arterial tree relative to the effect of the blood pressure pulse.