Abstract
Segmentation of cardiac fibrosis and scars is 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 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 [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] 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 utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
Highlights
Necrosis regions found in the heart (including left atrium (LA) pre-ablation fibrosis, LA postablation scar and left ventricle (LV) infarction), depending on the location and size, can have various implications on the cardiac conditions of the patients
3.1.2 Conventional Methods in Segmenting Anatomical Structures In the early 21st century, radiologists looked between Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) and cine CMR back and forth to delineate the myocardium region
By doing so, the result may suffer from registration misalignment between LGE and cine modalities and the model may be computationally demanding
Summary
Necrosis regions found in the heart (including left atrium (LA) pre-ablation fibrosis, LA postablation scar and left ventricle (LV) infarction), depending on the location and size, can have various implications on the cardiac conditions of the patients. Locating and quantifying the fibrosis and scars have been demonstrated as a valuable tool for the treatment stratification of patients with atrial fibrillation (AF) (Allessie, 2002; Boldt, 2004) or ventricular tachycardia (Ukwatta et al, 2015) and provide guidance information for the surgical or ablation based procedures (Vergara and Marrouche, 2011). LGE CMR has been shown to improve ablation strategy planning, treatment stratification and prognosis by pre-ablation fibrosis quantification via clinical validations (Akoum et al, 2011). It enabled computationally guided and personalized targeted ablation in treating AF in clinical practices (Boyle et al, 2019)
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