Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Joint segmentation and classification of retinal arteries/veins from fundus images

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin

Empirical Study of Deep Neural Network Architectures for Non-Rigid Medical Image Registration

Medical image registration is the alignment of two or more images of the same scene or object, but taken possibly from different viewpoints, at different times or by different sensors. Accurate registration plays an important role in the diagnosis and treatment of diseases. Several factors make the task of medical image registration challenging. The surface curvature of the tissues implies that the medical image registration is non-rigid and non-linear. Additionally, the quality of acquired images could be poor because of noise, inherent pathologies, low overlap area and repeated patterns. Recent development in computer vision and medical image processing has seen the introduction of transformer-based networks in accomplishing various tasks and with notable results. This trend has been seen in medical image registration where the performance of convolutional-based networks is being challenged by transformer-based networks. However, it is unclear that whether the improvement cited for transformer-based networks is due mainly to the architecture or other factors such as scale of transformation fields, dataset characteristics and the guidance of different loss functions. In this study, several deep neural network architectures are critically reviewed from the viewpoint of components of architectures, loss functions, scale of transformation fields and datasets respectively. Experiments involving ablation studies over several architectural options were designed and conducted to reveal the performance differences. Theoretical analyses are provided to interpret results