## 1 Introduction  
Myocarditis is the inflammation of myocardial tissue [1]. Myocarditis can affect the heart diffusively or focally, affecting different segments of the left ventricle (LV). Its clinical signs and symptoms are diverse, which makes diagnosis challenging [2]. Myocarditis is underdiagnosed due to the low sensitivity of cardiac biomarkers, ECG, and imaging modalities [3]. Myocarditis can only be confirmed by endomyocardial biopsy, which is seldom performed due to its invasive nature [4]. Echocardiography is non-specific for myocarditis and can show decreased LV function, thickened walls due to edema, segmental wall motion abnormalities, thrombus, and associated pericardial effusion. The golden non-invasive imaging standard for establishing a myocarditis diagnosis is cardiovascular magnetic resonance (CMR). This allows tissue characterization and the detection of focal myocardial involvement [5]. Meanwhile, late gadolinium enhancement (LGE) is an important parameter of myocardial injury, detecting expanded extracellular space due to myocyte necrosis, fibrosis, or edema [6]. Tracking LGE over time also helps guide treatment decisions [7]. Because myocarditis primarily affects the subepicardial or mid-LV areas [8], clinical experts determine the extent and regional distribution of myocardial damage by analyzing short-axis (SAx) LGE-CMR images (slices) of the LV (Figure 1), which cover the heart from base to apex. They use clinical experience and LGE image characteristics associated with myocarditis to identify bright regions that correspond to increased LGE accumulation [9], thus reflecting myocardial damage. During the image analysis, they segment the heart by identifying various regions of interest (ROIs) that depict pathological and normal myocardium. Then, by using the ROIs, they calculate the location and the extent of LGE by using mathematical quantification methods that distinguish between normal and pathological myocardium based on signal intensity (SI) differences between pixels belonging to each of them [6; 10]. The segmentation requires high levels of attention during ROI definition and this work is highly repetitive, repeated for up to 10 slices per ventricle, which altogether may increase errors. Furthermore, it is known that there is intraobserver and interobserver variation in the results of image analysis [6; 10; 11]. The variability originates in the subjective delineation of the ROIs. Because different ROIs may make different contributions to overall variability, it is of use to the specialist to know which delineation of ROIs creates the most error in the results. Thus, this study ranks all ROI delineation steps in terms of their contribution to overall LGE extent variability. In addition, inconclusive data on the preferred mathematical quantification method prevent standardization of the procedure. Clinical experts mostly use thresholding-based methods or the full width at half maximum (FWHM) method [6; 10; 11]. While thresholding methods use an SI threshold defined by standard deviations (\(\sigma\)) above a reference region selected in normal or dark myocardium [6], the FWHM method uses a threshold of above 50% of maximal SI within the reference region defined in pathological (bright) myocardium [10]. Figure 1: The left ventricle (LV) model and the orientation of short-axis late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images (slices). Existing evidence indicates that the FWHM method may be most appropriate for evaluating myocardial scarring in myocarditis patients, given its high prognostic value and low inter- and intraobserver variability [6; 10; 11; 12]. However, as thresholding methods are frequently used in practice, we decided to enhance our study by performing analysis using both the FWHM and the 3\(\sigma\) method. The latter has previously shown good reproducibility in quantifying myocardial scars of different etiologies [10] and is significantly associated with major adverse cardiovascular events (MACEs) in myocarditis patients [6].