Pranav Bhagirath

58 Chapter 3 A good contrast between normal myocardium, blood pool and infarct is challenging and greatly depends on achieving the optimal inversion time. Each scan in the image database was scored by five raters experienced in LGE CMR images. The rating with maximum votes determined the scan’s rating. Scans in the image database were ranked into three categories: good, average and poor. The Dice metric was computed separately in each category. This indicated how robust the algorithms were against contrast enhancement quality. RESULTS AND DISCUSSION Segmentation accuracy against consensus ground truth On the patient and porcine LGE CMR scans, segmentations from the algorithms were compared to the consensus ground truth. A consensus was available by combining segmentations from three separate observers. Segmentation accuracies measured using the Dice metric are shown in figure 3 for the patient dataset. The Dice overlaps between algorithm and consensus were determined on an automatically determined region-of-interest (ROI) enclosing each individual region of infarction labeled in the consensus. The medians of these individual Dice overlaps were as follows: AIT = 73, KCL = 74, MCG = 85, MV = 44, and UPF = 70. Fixed model approaches for segmenting scar (i.e. n-SD and FWHM) were also compared with the consensus ground-truth. The median Dice overlaps were: 2-SD = 47, 3-SD = 54, 4-SD = 55, 5-SD = 62, 6-SD = 64, FWHM = 78. An example of a single slice from the patient dataset is shown in figure 5 . Figure 3. Performance on patient datasets: segmentation accuracy on the patient dataset. The figure also displays results from 2-SD, 3-SD, 4-SD, 5-SD, 6-SD and FWHM. Dice was computed on every individual region of scar found in the consensus segmentation.