Pranav Bhagirath

44 Chapter 3 Recent studies have also demonstrated how infarct size, shape and location from pre- procedural LGE can be useful in guiding ventricular tachycardia’s (VT) ablation (Estner et al., 2011; Andreu et al., 2011). These procedures are often time-consuming due to the preceding electrophysiological mapping study required to identify slow conduction zone involved in re-entry circuits. Post-processed LGE images provide scar maps, which can be integrated with electroanatomic mapping systems to facilitate these procedures (Andreu et al., 2011). Clinical implementation of these developments necessitates a reliable, fast, reproducible and accurate segmentation of the infarcted region. Moreover, as use of LGE-based infarct volume estimation becomes more clinically relevant, standardization will facilitate more consistent interpretation. STATE-OF-THE-ART FOR CARDIAC INFARCT SEGMENTATION A short overview of previously published infarct detection algorithms for the left ventricle (LV) is presented here. Table 1 lists the algorithms surveyed and highlights some of their important features. A common method for detecting infarct in the LV is the fixed-model approach, whereby intensities are thresholded to a fixed number of standard deviations (SD) from the mean intensity of nulled myocardium or blood pool (Flett et al., 2011). In the rest of the paper this will be known as the n-SD method, where n = 2, 3, 4, 5 or 6. A second common fixed-model approach is the full-width-at- half-maximum (FWHM) approach, where half of the maximum intensity within a user- selected hyper-enhanced region is selected as the fixed intensity threshold (Amado et al., 2004). Using this threshold, a region-growing process is employed from user- selected seeds. These seeds are selected to be within infarcted regions such that they can be segmented with region-growing. As the aforementioned approaches require user input, making them prone to inter- and intra-observer variation, other approaches that are automatic have been developed. Hennemuth et al. (2008) modeled the intensities of homogeneous tissue in LGE CMR with a Rician distributions and an expectation-maximization (EM) algorithm was used for fitting the data. Pop et al. (2013) fitted Gaussian mixture models to myocardial tissue pixel intensities and correlated with histology. In Detsky et al. (2009), clustering in a feature space of steady-state and T*1 intensity values provided the segmentation which was shown to provide good correlation with FWHM. Tao et al. (2010) employed automatic thresholding using the Otsu method on bi-modal intensity histograms of myocardium and blood pool. More recently, the use of the graph-cut technique in image processing has been applied to segment infarct in several methods (Lu et al., 2012; Karim et al., 2014; Karimaghaloo et al., 2012). An advantage of this technique is