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

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease

A Computational Tool for Quantitative Analysis of Vascular Networks

Angiogenesis is the generation of mature vascular networks from pre-existing vessels. Angiogenesis is crucial during the organism' development, for wound healing and for the female reproductive cycle. Several murine experimental systems are well suited for studying developmental and pathological angiogenesis. They include the embryonic hindbrain, the post-natal retina and allantois explants. In these systems vascular networks are visualised by appropriate staining procedures followed by microscopical analysis. Nevertheless, quantitative assessment of angiogenesis is hampered by the lack of readily available, standardized metrics and software analysis tools. Non-automated protocols are being used widely and they are, in general, time - and labour intensive, prone to human error and do not permit computation of complex spatial metrics. We have developed a light-weight, user friendly software, AngioTool, which allows for quick, hands-off and reproducible quantification of vascular networks in microscopic images. AngioTool computes several morphological and spatial parameters including the area covered by a vascular network, the number of vessels, vessel length, vascular density and lacunarity. In addition, AngioTool calculates the so-called “branching index” (branch points / unit area), providing a measurement of the sprouting activity of a specimen of interest. We have validated AngioTool using images of embryonic murine hindbrains, post-natal retinas and allantois explants. AngioTool is open source and can be downloaded free of charge

Fully automatized parallel segmentation of the optic disc in retinal fundus images

This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani’s technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency.Ministerio de Economía y Competitividad TIN2012-3743

Innovative Tool for Automatic Detection of Arterial Stenosis on Cone Beam Computed Tomography

Arterial stenosis is one of the main vascular diseases that are treated with minimally invasive surgery approaches. The aim of this study was to provide a tool to support the medical doctor in planning endovascular surgery, allowing the rapid detection of stenotic vessels and the quantification of the stenosis. Skeletonization was used to improve vessels’ visualization. The distance transform was used to obtain a linear representation of the diameter of critical vessels selected by the user. The system also provides an estimate of the exact distance between landmarks on the vascular tree and the occlusion, important information that can be used in the planning of the surgery. The advantage of the proposed tool is to lead the examination on the linear representation of the chosen vessels that are free from tortuous vascular courses and from vessel crossings

Blood flow rate estimation in optic disc capillaries and vessels using Doppler optical coherence tomography with 3D fast phase unwrapping

The retinal volumetric flow rate contains useful information not only for ophthalmology but also for the diagnosis of common civilization diseases such as diabetes, Alzheimer's disease, or cerebrovascular diseases. Non-invasive optical methods for quantitative flow assessment, such as Doppler optical coherence tomography (OCT), have certain limitations. One is the phase wrapping that makes simultaneous calculations of the flow in all human retinal vessels impossible due to a very large span of flow velocities. We demonstrate that three-dimensional Doppler OCT combined with three-dimensional four Fourier transform fast phase unwrapping (3D 4FT FPU) allows for the calculation of the volumetric blood flow rate in real-time by the implementation of the algorithms in a graphics processing unit (GPU). The additive character of the flow at the furcations is proven using a microfluidic device with controlled flow rates as well as in the retinal veins bifurcations imaged in the optic disc area of five healthy volunteers. We show values of blood flow rates calculated for retinal capillaries and vessels with diameters in the range of 12-150 µm. The potential of quantitative measurement of retinal blood flow volume includes noninvasive detection of carotid artery stenosis or occlusion, measuring vascular reactivity and evaluation of vessel wall stiffness