Whole Heart and Great Vessel Segmentation in Congenital Heart Disease Using Deep Neural Networks and Graph Matching

AbstractEfficient cardiac pumping depends on the morphological structure of the heart, but also on its sub-cellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized sub-cellular disruptions in architecture that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three dimensional (3D) heart structure, assessing malformations; and its ultrastructure, assessing disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). This combination of technologies has not been possible before in imaging the same cardiac sample due to the heart large size, even when studying small fetal and neonatal animal models (~5×5×5mm3). Here, we achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation (at the nm resolution range). Our approach enables multiscale studies of cardiac architecture in models of congenital heart disease and beyond.

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Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

Reconstruction, Segmentation, and Analysis of Medical Images - Lecture Notes in Computer Science ◽

10.1007/978-3-319-52280-7_9 ◽

2017 ◽

pp. 95-102 ◽

Cited By ~ 27

Author(s):

Jelmer M. Wolterink ◽

Tim Leiner ◽

Max A. Viergever ◽

Ivana Išgum

Keyword(s):

Neural Networks ◽

Congenital Heart Disease ◽

Heart Disease ◽

Convolutional Neural Networks ◽

Congenital Heart ◽

Cardiovascular Mr ◽

Mr Segmentation

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Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register based medical hospital data and neural networks

European Heart Journal - Digital Health ◽

10.1093/ehjdh/ztab065 ◽

2021 ◽

Author(s):

Kok Wai Giang ◽

Saga Helgadottir ◽

Mikael Dellborg ◽

Giovanni Volpe ◽

Zacharias Mandalenakis

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Neural Networks ◽

Congenital Heart Disease ◽

Logistic Regression ◽

Heart Disease ◽

Congenital Heart ◽

Hospital Data ◽

Neural Network Models ◽

Over Time

Abstract Aims To improve short-and long-term predictions of mortality and atrial fibrillation among patients with congenital heart disease from a nationwide population using neural networks. Methods and results The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with congenital heart disease born from 1970 to 2017. A total of 71,941 congenital heart disease patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a neural network model was obtained to predict mortality and atrial fibrillation. Logistic regression based on the same data was used as a baseline comparison. Of 71,941 congenital heart disease patients, a total of 5768 died (8.02%) and 995 (1.38%) developed atrial fibrillation over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of neural network models in predicting the mortality and atrial fibrillation was higher than the performance of logistic regression regardless of the complexity of the disease, with an average Area Under the Receiver Operating Characteristic of > 0.80 and >0.70, respectively. The largest differences were observed in mortality and complexity of congenital heart disease over time. Conclusion We found that neural networks can be used to predict mortality and atrial fibrillation on a nationwide scale using data that are easily obtainable by clinicians. In addition, neural networks showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.

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