Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging

Abstract

BackgroundSegmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods—particularly deep learning (DL)—can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT.MethodsEmbase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).Results18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83–0.91), left ventricle (0.91, IQR 0.89–0.94), left ventricle myocardium (0.83, IQR 0.82–0.92), right atrium (0.88, IQR 0.83–0.90), right ventricle (0.91, IQR 0.85–0.92), and pulmonary artery (0.92, IQR 0.87–0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%).ConclusionSupervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings.Systematic Review Registration[www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113].

Synergizing Deep Learning-Enabled Preprocessing and Human–AI Integration for Efficient Automatic Ground Truth Generation

“…Deep learning and convolutional neural networks have proven to be highly effective and useful tools in medical image analysis, including MRI, CT, PET, and histopathology images [1][2][3][4][5][6][7][8]. Even so, expert labeling of medical image datasets to generate ground truth for training deep learning algorithms is still an ongoing challenge to the adoption of artificial intelligence in medical practice [7].…”