Abstract:

As the aging population increases, the prevalence of cardiovascular disease rises annually. In this context, the evaluation of cardiac function using medical imaging techniques plays a pivotal role in the diagnosis and treatment of cardiovascular disease. Cardiac segmentation is a prerequisite for assessing cardiac function and has been closely studied by clinicians and scientific researchers. This paper provides a comprehensive review of the literature from the past decade on cardiac segmentation, categorizing the studies into traditional segmentation approaches and deep learning methodologies. Emphasis is placed on the detailed discussion of segmentation methods based on active contours and atlas models; deep learning algorithms based on U-Net and full convolution neural network (FCN) are also extensively discussed. In particular, this paper elaborates various approaches to enhance deep learning networks and achieve accurate segmentation of specific cardiac regions. These approaches include incorporating local modules, optimizing loss functions, and enhancing network architectures. A comprehensive summary of the aforementioned methods is presented, considering three imaging modalities: cardiac magnetic resonance imaging, computed tomography, and ultrasonic cardiogram. Lastly, the article concludes by summarizing the current research status and discussing research directions for further exploration.

Key words: cardiac image segmentation, deep learning, U-Net, FCN