Deep neural network architectures for cardiac image segmentation

Abstract

Imaging plays a fundamental role in the effective diagnosis, staging, management, and monitoring of various cardiac pathologies. Successful radiological analysis relies on accurate image segmentation, a technically arduous process, prone to human-error. To overcome the laborious and time-consuming nature of cardiac image analysis, deep learning approaches have been developed, enabling the accurate, time-efficient, and highly personalised diagnosis, staging and management of cardiac pathologies. Here, we present a review of over 60 papers, proposing deep learning models for cardiac image segmentation. We summarise the theoretical basis of Convolutional Neural Networks, Fully Convolutional Neural Networks, U-Net, V-Net, No-New-U-Net (nnU-Net), Transformer Networks, DeepLab, Generative Adversarial Networks, Auto Encoders and Recurrent Neural Networks. In addition, we identify pertinent performance-enhancing measures including adaptive convolutional kernels, atrous convolutions, attention gates, and deep supervision modules. Top-performing models in ventricular, myocardial, atrial and aortic segmentation are explored, highlighting U-Net and nnU-Net-based model architectures achieving state-of-the art segmentation accuracies. Additionally, key gaps in the current research and technology are identified, and areas of future research are suggested, aiming to guide the innovation and clinical adoption of automated cardiac segmentation methods.