Myocardial scar, a non-viable tissue which forms in the myocardium due to insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Accurate reconstruction of myocardial scar geometry is important for diagnosis and clinicial prognosis of the patients with ischemic cardiomyopathy. The 3D late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is increasingly being investigated for assessing myocardial tissue viability. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, segmentation and reconstruction of the intact geometry of scar is required. However, manual analysis and segmentation of myocardial scar from 3D LGE-MRI is a tedious task. Therefore, semi-automated and fully-automated segmentation algorithms are highly desirable in a clinical setting. In this study, we developed an approach to segment the myocardial scar from 3D LGE-MR images using a continuous max-flow (CMF) method. The data term comprised of a distribution matching term for scar and normal myocardium and a boundary smoothness term for the scar boundaries. The region-of-interest for the scar segmentation is constrained, using manually segmented myocardium. We evaluated our CMF method for accuracy by comparing it to manual scar delineations using 3D LGE-MR images of 34 patients. We compare the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yielded a Dice similarity coefficient (DSC) of 72±18% and an absolute volume error (|V E|) of 15.42±14.1 cm3. Overall, the CMF method outperformed the state-of-the-art methods for all reported metrics in 3D scar segmentation except for the recall value which STRM 2-SD performed better than CMF on average.