Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN was trained using 32 manually annotated centerlines in a training set consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93.7% with 96 manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. In a second test set consisting of 50 CCTA scans, 5,448 markers in the coronary arteries were used as seed points to extract single centerlines. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans, fully automatic seeding and centerline extraction led to extraction of on average 92% of clinically relevant coronary artery segments. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi

Evoked Potentials in Diabetic Syndrome of Rats Before and After Two Months of Methadone Treatment

The effect of two months treatment with methadone (0.87 mg/kg per os, daily) on the somatosensory evoked potentials (SEPs) in female control and diabetic rats were studied. Diabetes was induced by alloxan monohydrate (65 mg/kg i. v.). SEPs obtained after electrical stimulation of the contralateral forepaw and recorded from the scalp, by an non invasive method, showed high reproducibility. Prolonged latency (156%) and a marked amplitude enhancement (371%) characterized the mid-latency SEPs following 64 days methadone treatment of healthy rats. Experimental diabetes induced significant alteration of both parameters; amplitude decrease of earlier components (Nl, P2) and delayed latency of later ones (P3). Two months methadone treatment of diabetic rats not only leads to the recovery but also augmented P2 (P25) amplitude (225%). It was concluded that no essential differences exist in the methadone effect on control (healthy) and diabetic Wistar female rats

Razvoj lokalne demokracije u Hrvatskoj: Institucionalna reforma izbora lokalne izvršne vlasti kao instrument demokratskog razvitka hrvatskog društva : diplomski rad

U ovom radu analizirat će se rezultati posljednje institucionalne promjene načina izbora lokalnih čelnika kao instrumenta demokratskog razvitka hrvatskog društva. Provjerit će se doprinose li neposredni izbori izvršne vlasti na lokalnoj razini jačanju demokratskih procesa i institucija

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening

Upravljanje robotom pomoću anticipacijskih potencijala mozga

Recently Biomedical Engineering showed advances in using brain potentials for control of physical devices, in particular, robots. This paper is focused on controlling robots using anticipatory brain potentials. An oscillatory brain potential generated in the CNV Flip-Flop Paradigm is used to trigger sequence of robot behaviors. Experimental illustration is given in which two robotic arms, driven by a brain expectancy potential oscillation, cooperatively solve the well known problem of Towers of Hanoi.U posljednje vrijeme je u području biomedicinskog inženjerstva postignut napredak u korištenju potencijala mozga za upravljanje fizičkim napravama, posebice robotima. U radu je opisana mogućnost upravljanja robotima pomoću anticipacijskih potencijala mozga. Oscilacijski potencijal mozga generiran u CNV (Contingent Negative Variation) flip-flop paradigmi se koristi za okidanje slijeda ponašanja robota. U radu je prikazana eksperimentalna ilustracija rješavanja dobro poznatog problema Hanojskih tornjeva pomoću dvije robotske ruke upravljane moždanim potencijalom očekivanja

Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol