Segmentation of Scarred Myocardium in Cardiac Magnetic Resonance Images

The segmentation of scarred and nonscarred myocardium in Cardiac Magnetic Resonance (CMR) is obtained using different features and feature combinations in a Bayes classifier. The used features are found as a local average of intensity values and the underlying texture information in scarred and nonscarred myocardium. The segmentation classifier was trained and tested with different experimental setups and parameter combinations and was cross validated due to limited data. The experimental results show that the intensity variations are indeed an important feature for good segmentation, and the average area under the Receiver Operating Characteristic (ROC) curve, that is, the AUC, is 91.58 ± 3.2%. The segmentation using texture features also gives good segmentation with average AUC values at 85.89 ± 5.8%, that is, lower than the direct current (DC) feature. However, the texture feature gives robust performance compared to a local mean (DC) feature in a test set simulated from the original CMR data. The segmentation of scarred myocardium is comparable to manual segmentation in all the cross validation cases. 1. Introduction After a myocardial infarction (MI), the myocardium does not function properly due to scarring of the tissue. Late Gadolinium Enhanced Cardiac Magnetic Resonance (LGE-CMR) imaging is used for assessing morphology of myocardium after an MI. Segmenting the scarred areas from the healthy myocardium is an important prerequisite for various diagnostic analyses. For example, the scar size is largely responsible in left ventricular remodeling [1]. The scar size and texture, as well as the left ventricle ejection fraction, are also important to identify patients with high risk of getting life threatening arrhythmias and decide who will benefit from implantation of implantable cardioverter-defibrillator (ICD) [2]. The scar size is also related to the heart rate of ventricular tachycardia [3]. Earlier work focuses mostly on manual or semiautomatic methods for segmenting the scarred area [2, 4]. At Stavanger University Hospital (SUS), as in many other hospitals today, the cardiologists will provide their input in a semiautomatic system to segment the heart muscle from the surrounding areas as well as to segment scarred from healthy areas. This is a time consuming job and will be vulnerable to inter-observer variability. In recent years, there has been some publications on fully automatic methods for segmenting the myocardial muscle [5, 6] and also methods to segment the scarred areas from the healthy parts of the heart muscle in LGE-CMR images [5,