Integrating statistical cardiac atlases with deep neural networks improves the anatomical consistency and performance of cardiac segmentation, registration, and automated quality control.
In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.
Gilbert et al. (2020) conducted a review in Cardiac remodeling and disease. Artificial Intelligence with Statistical Cardiac Atlases was evaluated. Integrating statistical cardiac atlases with deep neural networks improves the anatomical consistency and performance of cardiac segmentation, registration, and automated quality control.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: