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

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Lesion boundary segmentation using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth

A consensus definition for lamellar macular hole

Background: A consensus on an optical coherence tomography (OCT) definition of lamellar macular hole (LMH) and similar conditions is needed. Methods: The panel reviewed relevant peer-reviewed literature to reach a consensus on LMH definition and to differentiate LMH from other similar conditions. Results: The panel reached a consensus on the definition of 3 clinical entities: LMH, epiretinal membrane (ERM) foveoschisis and macular pseudohole (MPH). LMH definition is based on 3 mandatory criteria and 3 optional anatomical features. The 3 mandatory criteria are the presence of irregular foveal contour, the presence of a foveal cavity with undermined edges, and the apparent loss of foveal tissue. Optional anatomical features include the presence of epiretinal proliferation, the presence of a foveal bump and the disruption of the ellipsoid line. ERM foveoschisis definition is based on 2 mandatory criteria: the presence of ERM and the presence of schisis at the level of Henle’s fiber layer. Three optional anatomical features can also be present: the presence of microcystoid spaces in the inner nuclear layer (INL), an increase of retinal thickness, and the presence of retinal wrinkling. MPH definition is based on 3 mandatory criteria and 2 optional anatomical features. Mandatory criteria include the presence of a foveal sparing ERM, the presence of a steepened foveal profile and an increased central retinal thickness. Optional anatomical features are the presence of microcystoid spaces in the INL and a normal retinal thickness. Conclusions: The use of the proposed definitions may provide uniform language for clinicians and future research

OCT-Based Macular Structure-Function Correlation in Dependence on Birth Weight and Gestational Age : the Giessen Long-Term ROP Study

Abstract Purpose: To compare retinal layer thicknesses in preterm and term-born children using spectral-domain optical coherence tomography (SD-OCT) and to correlate structure with retinal function. Methods: We performed SD-OCT single and volume scans in the foveal region of premature children aged 6 to 13 years without ROP (no-ROP, n = 100) and with spontaneously regressed ROP (sr-ROP, n = 50) documented with wide-angle digital imaging during routine screening for acute ROP, and 30 age-matched term-born children. Retinal layer segmentation and analysis was performed with custom-made software in single and volume-scans using an Early Treatment of Diabetic Retinopathy Study grid-based method, and compared to light increment sensitivity (LIS) data obtained with a microperimeter at eccentricity points of 0°, 2.8°, and 8°, as previously described. Results: Overall, seven children had to be excluded due to poor image quality (n = 1 no-ROP; n = 2 sr-ROP; n = 4 term). Total retina, ganglion cell + inner plexiform layer (GCL+) and outer nuclear layer + external limiting membrane (ONL+) thickness at the foveal center in no-ROP and sr-ROP were significantly higher compared with term children. Gestational age (GA) and birth weight (BW) were inversely correlated with these layer thicknesses. Rod and cone outer segment length did not differ in either group. The ratio of ONL+ to the whole retina at 0° correlated significantly with reduced LIS. Conclusions: Increased thicknesses of the entire retina or specific layers at the fovea did not correlate with functional loss; but a thinner ONL in retinae without foveal pit did. This reduced ONL+ ratio is potentially caused by a reduced foveal cone density and may be the first morphologic functional correlate in prematurity and ROP