No abstract

Background: In the era of increasingly successful corrective interventions in patients with congenital heart disease (CHD), global and regional myocardial remodeling are emerging as important sources of long-term morbidity/mortality. Changes in organization of the myocardium in CHD, and in its mechanical properties, conduction, and blood supply, result in altered myocardial function both before and after surgery. To gain a better understanding and develop appropriate and individualized treatment strategies, the microscopic organization of cardiomyocytes, and their integration at a macroscopic level, needs to be completely understood. The aim of this study is to describe, for the first time, in 3 dimensions and nondestructively the detailed remodeling of cardiac microstructure present in a human fetal heart with complex CHD. Methods and Results: Synchrotron X-ray phase-contrast imaging was used to image an archival midgestation formalin-fixed fetal heart with right isomerism and complex CHD and compare with a control fetal heart. Analysis of myocyte aggregates, at detail not accessible with other techniques, was performed. Macroanatomic and conduction system changes specific to the disease were clearly observable, together with disordered myocyte organization in the morphologically right ventricle myocardium. Electrical activation simulations suggested altered synchronicity of the morphologically right ventricle. Conclusions: We have shown the potential of X-ray phase-contrast imaging for studying cardiac microstructure in the developing human fetal heart at high resolution providing novel insight while preserving valuable archival material for future study. This is the first study to show myocardial alterations occur in complex CHD as early as midgestation.

We have developed a novel, high resolution, image acquisition, and quantification approach to study a whole in-vitro heart at myofibre resolution, providing integrated 3D structural information at microscopic level without any need of tissue slicing and processing. This superior imaging approach opens up new possibilities for a systems approach towards analysing cardiac structure and function, providing rapid acquisition of quantitative microstructure of the heart in a near native state.

Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.

Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test our method on publiclyavailable DRIVE dataset and our results demonstrate the high effectiveness of the deep learning approach. Our method achieves an average accuracy and AUC of 0.9466 and 0.9749, respectively.