Pranav Bhagirath

68 Chapter 3 regions can be biased. However, it is important to note that the Dice overlap used here was slice-based and not region-based as compared to the other results in this work. In general, with Dice scores, it is difficult to ascertain a good Dice for datasets of the nature included in this study. The analysis of agreement between the observers’segmentations (see figure 8 ) provide for a reasonable estimation and target for the algorithms. The algorithm’s’ comparison to common algorithms is important. The difference with FWHM remains small except for MCG, which was able to provide high accuracy in the patient dataset, and AIT providing the same in the porcine set. Both methods have considerable strengths, with the former using a state-of-the-art probabilistic technique for image segmentation, and the latter benefitting from post-processing steps which rectify the segmentation. The Dice results reflect the strengths of these methods. On the patient datasets, algorithms AIT, MCG and UPF performed similarly while KCL and MV also performed similarly but with a lower average Dice. This was due to greater variability in Dice for KCL and MV. However, AIT and UPF are both capable of rectifying errors in its segmentation with post-processing steps. AIT employs level-sets following SVM classification and UPF employs shape discriminants. Both KCL and MV rely heavily on its core segmentation process, with no post-processing. As a result, spurious regions are included. Models that are sub-optimal were able to benefit from post-processing. The algorithms were also evaluated on the total infarct volume it segmented (see Table 4 ) and these volumes were compared to the consensus volumes. This is important as Dice computed in this work has the aforementioned limitations. Also, when evaluating the myocardium, quantification of infarct volume is an important step. The average volume error in challenger’s algorithms were 1.04 ml and 0.76 ml for patient and porcine datasets respectively (from Table 4 ). This was low compared to the overall average infarct volume in the datasets (see Table 6 ). Table 6. The mean infarct volume (in milliliters) and average number of regions (i.e. infarct) per slice in the consensus segmentation. Patient data Porcine data Mean infarct volume (ml) 5.38 (6.73) 13.81 (8.70) Average regions per slice 1.2 (0.5) 1.0 (0.1) The algorithms evaluated on the framework have common traits – most employ region- based image processing techniques, for example level-set (AIT), region-growing (UPF and FWHM) and watershed (MV). This is justifiable as the algorithms are meant to