  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.
