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

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality

Deep Learning-based Prescription of Cardiac MRI Planes.

PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

One physical sign of a person who has Alzheimer's is the diminution of the area of the hippocampus and ventricles. A good quality magnetic resonance imaging (MRI) will provide a high-quality image so that the doctor will quickly analyze the abnormalities of the hippocampus and ventricle area. However, for low-quality MRI, this is difficult to do. This condition will be a significant problem for some regions in developing countries including Indonesia, where many hospitals have only low-quality MRI, and many hospitals do not have them at all. The primary purpose of this research is to develop simple tools to analyze morphological characteristics in Alzheimer's patients. In this paper, we focus only on coronal slice analysis. We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image. Analysis of morphological characteristics is also classified by age and gender. Then by referring to the value of the clinical dementia rating (CDR), the process of identifying between images with Alzheimer's disease (AD) and healthy models is done based on the morphological analysis that has been done. The results show this method has a better performance compared to the previously work

Study of the correlation between bicuspid aortic valve and the development of aortic dissection

La disección aórtica (AD) es la condición letal más comúnmente diagnosticada de la arteria aorta y consiste en el redireccionamiento del flujo sanguíneo desde el lumen de la aorta hasta la media de la pared de la aorta a través de una pequeña fisura en la intima. Las causas específicas de la formación de esta fisura, y de la subsecuente dilatación de la pared, todavía no han sido completamente determinadas aunque diversos estudios muestran que puede ser debida o bien a cambios químicos o bien a efectos mecánicos en la pared de la aorta. Este trabajo se centra en el estudio de posibles efectos mecánicos, inducidos por cambios en la hemodinámica de la arteria, que puedan haber conducido al debilitamiento de la pared de la aorta. Válvula aórtica bicúspide (BAV) es la enfermedad congénita del corazón más común y se ha demostrado su importante contribución en el desarrollo de numerosas condiciones cardiovasculares. Esta enfermedad modifica el orificio de salida del corazón, y por tanto el perfil hemodinámico de eyección, del flujo de sangre, lo que podría tener consecuencias en el comportamiento mecánico de la pared de la aorta. Este estudio tiene como objetivo determinar que existe una correlación entre los cambios en la hemodinámica producidos por la presencia de BAV y la formación de AD usando técnicas de análisis de dinámica de fluidos computacional (CFD). Para determinar dicha relación, análisis CFD se han realizado en tres geometrías diferentes: un caso de válvula aórtica tricúspide (TAV) y dos casos distintos de BAV. Todas las geometrías son idealizadas y contemplan la raíz de la aorta, la aorta ascendente y el comienzo del cayado aórtico. Los resultados de los análisis muestran un incremento en la velocidad de eyección de la sangre para ambos casos de BAV debido a la reducción en el área efectiva del orificio. Además, el estudio muestra un incremento en las fuerzas de rozamiento de la pared y en la presión de la pared externa de la aorta. Estos resultados nos llevan a la conclusión de que BAV podría causar hipertensión en la pared externa de la aorta, la cual es una causa mecánica conocida del debilitamiento de vasos sanguíneos