Automating vessel segmentation in the heart and brain: A trend to develop multi-modality and label-efficient deep learning techniques

Abstract

Cardio-cerebrovascular diseases remain the leading cause of mortality worldwide, making accurate blood vessel segmentation essential for both scientific research and clinical applications. However, segmenting cardio-cerebrovascular structures from medical images is highly challenging due to factors such as thin or blurred vascular shapes, imbalanced vessel-to-background pixel distribution, and interference from imaging artifacts. These difficulties render manual or semi-manual segmentation methods time-consuming, labor-intensive, and prone to inter-observer variability. Consequently, there is an increasing demand for automated segmentation algorithms. This paper presents the first comprehensive survey of deep learning techniques for cardio-cerebrovascular segmentation, covering supervised, semi-supervised, and unsupervised approaches for both cardiac and cerebral vasculature. We review state-of-the-art methods, including U-Net, Generative Adversarial Networks (GANs), Graph Convolutional Networks (GCNs), transformer models, diffusion models such as Denoising Diffusion Probabilistic Models (DDPM), foundation models like Segment Anything Model (SAM) and the SAM-VMNet models, as well as hybrid approaches combining multiple models with effective fusion techniques. We discuss the strengths and limitations of these methods, emphasizing their clinical applicability. Our analysis identifies key challenges, including the reliance on annotated data and the limitations of single-modality approaches, which fail to fully leverage the rich information available from multi-modal imaging sources. We highlight the importance of label-efficient, multi-modal deep learning as a promising direction for improving segmentation accuracy and robustness. This survey provides valuable insights for researchers and clinicians, aiming to guide the development of next-generation tools for more accurate diagnoses and personalized treatment strategies for cardio-cerebrovascular diseases.