Pranav Bhagirath

70 Chapter 3 et al., 2007). This work proposes an evaluation framework for future algorithms which segment and quantify LV infarct. To demonstrate its usability, five different algorithms were evaluated on the framework. Three of which have been published (Hennemuth et al., 2008; Karimaghaloo et al., 2012; Karim et al., 2014). Six different fixed-model approaches were also evaluated. The framework provides thirty datasets, of which ten are for algorithm training and the rest for testing. Although they represent a specific pulse sequence, some algorithms evaluated here could be re-trained on new sequences. The consensus ground truths are derived frommanual segmentations of three separate observers. Future algorithms can be evaluated both objectively with overlap metrics or less objectively and conventionally with pixel volumes. Most importantly, they can be compared and benchmarked against existing algorithms. To our knowledge, this is the first proposed framework for evaluating LV infarct segmentation and quantification algorithms from LGE CMR images. For the left atrium, a benchmarking evaluation framework already exists (Karim et al., 2013). CONCLUSION CMR continues to play an important role in imaging and quantifying infarct in the LV. Several algorithms have been proposed for its quantification but it is not clear how they compare or perform relative to one another. Furthermore, algorithms have only been tested on center- and vendor-specific images. The translation of such algorithms into the clinical environment thus remains challenging. Benchmarking frameworks, providing a common dataset and evaluation strategies, is important for clinical translation of these algorithms. The proposed benchmarking framework provides thirty datasets, with fifteen datasets in each cohort: patient and porcine. Datasets in the two separate cohorts were acquired using different scanner vendors and field strength (1.5T and 3T), resolutions and acquisition protocols (2D and 3D). The ground truth is often absent in such datasets, and to this end, the framework provides with a powerful expert observers’ consensus ground truth. The proposed framework remains publicly available for accessing the image database, uploading segmentations for evaluation and contributing manual segmentations for improving the consensus ground truth on the datasets.