Pranav Bhagirath

49 Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images Figure 1. Sample datasets: a sample of LGE CMR data included in the challenge. The human (top-row) and porcine (bottom-row) images are shown. Algorithm 1: Alma IT Systems - support vector machines and level sets (AIT) Background: Support vector machines (SVM) and level set methods were used to segment scar in this method. SVM is a machine learning technique which first computes the optimal hyperplane on a set of training data mapped to some feature space (Hearst et al., 1998). The hyperplane is a decision boundary which maximally separates the pre- labeled data. Once the hyperplane is obtained, the unseen data is mapped to the same feature space to see which side of the hyperplane it lies in. This labels and thus classifies the unseen data. Level-sets (Sethian, 1999) were also used in this method. In this technique a region evolves from an initial position within the region to be segmented. Level-sets have the added advantage of imposing shape constraints on the evolving region.