Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives

TL;DR

This paper reviews recent advances in cardiac fibrosis and scar segmentation from MRI, highlighting conventional and deep learning methods, and discusses future directions for improving accuracy and efficiency in clinical diagnosis.

Contribution

It provides a comprehensive overview of current segmentation techniques, including traditional and deep learning approaches, and explores multimodal integration for enhanced accuracy.

Findings

Deep learning methods outperform traditional techniques in accuracy.

Multimodal co-registration improves segmentation reliability.

Recent methods show increased efficiency and clinical applicability.

Abstract

Segmentation of cardiac fibrosis and scar are essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities, balanced steady-state free precession (bSSFP) and cine magnetic resonance imaging (MRI) for example, can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilising different modalities for accurate cardiac fibrosis and scar segmentation.