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Table 3 A brief summary of algorithms that were evaluated on the proposed framework

From: Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

Algorithm Technique Evaluation Atrial wall Strengths Weaknesses
IC: Bai et al. Hysteresis thresholding 30 pre and post Euclidean distance - 3 mm Coherent segmentations Fixed sigmoid models derived from empirical data
MV: Hennemuth et al. Region-growing with EM-fitting 30 pre and post Euclidean distance - 3 mm Post ablation imaging Pre-ablation imaging
SY: Lu et al. MRF model with graph-cuts 20 pre and post Dilation - 4 mm Fuzzy membership - improved delineation Post-processing for small cluster removal
HB: Gao et al. Active contour and EM-fitting 15 post Active contour (snake) Accurate myocardial segmentation Fixed number of gaussian mixtures in model (i.e. two)
YL: Peters et al. Simple thresholding 15 pre and post Manual Accurate segmentation on both pre- and post. Time consuming
KCL: Karim et al. MRF model with graph-cuts 30 pre and post Post-ablation imaging Pre-ablation imaging Post-processing steps necessary
UTA: Cates et al. Histogram analysis and simple thresholding 30 pre and post Manual Accurate segmentation on pre and post. Time consuming
UTB: Perry et al. k-means clustering 30 pre and post Manual Pre-ablation fibrosis Equivalent variance across all clusters - LA scar variance more variable
  1. Institution abbreviations: IC Imperial College, MV Mevis Fraunhofer, SY Sunnybrook Toronto, HB Harvard/Boston University, YL Yale School of Medicine, KCL King’s College London, UTA/B Utah School of Medicine.