Pranav Bhagirath

65 Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images DISCUSSION We have presented a framework which standardizes evaluation of algorithms for segmenting scar in the LV. The framework was used to evaluate and compare five algorithms and six separate fixed model thresholding approaches (i.e. n−SD and FWHM). The algorithms were submitted as part of the STACOM challenge, a workshop organized at MICCAI in 2012. The data is publicly available via the website at: https:// www.cardiacatlas.org/web/guest/ventricular-infarction-challenge. Evaluation framework The presented evaluation framework comprises of both human and animal LV LGE CMR datasets and their respective myocardial segmentation masks. Human datasets were acquired from patients with a history of ischemic cardiomyopathy. The animal datasets were acquired in a pig model of myocardial infarction induced by coronary stenosis. Datasets were also acquired using different scanner vendors and resolutions. The human datasets were acquired with a 1.5T Philips scanner and the animal datasets were acquired with a 3T Siemens scanner. There were both 2D and 3D (non-isotropic) acquisitions. This ensured that algorithms evaluated on the framework were not biased to a specific acquisition protocol, scanner vendor or resolution. The proposed framework provides data acquisitions that are both commonly-used andmodern, making it suitable for testing and evaluating state-of-the-art algorithms. It is often challenging to establish ground truth on infarcted regions in LGE CMR. This makes algorithm evaluation difficult. The framework addresses this issue by proposing a reference standard against which the algorithms can be reliably evaluated. To achieve a reference standard, the human and animal datasets were manually segmented by three experienced observers provided with epi- and endocardial boundaries and a set of guidelines. Although, their delineations were consistent, some differences remained. The three expert delineations were combined to obtain a consensus segmentation of all three observers. The STAPLE algorithm (Warfield et al., 2004), which uses a probabilistic estimate of the true segmentation to derive the consensus, was used to obtain a consensus segmentation. The degree of agreement between observers and the computed consensus was analyzed in Fig. 8 and this not only allows the assessment of agreement but also quantitatively provides for an estimation of a good Dice score in such datasets. In addition to the reference standard for scar, six commonly-used and established fixed thresholding models were used to see how they compare with the algorithms. These were namely the n-SD (where n = 2, 3, 4, 5, 6) and FWHM methods (Amado et al., 2004; Schmidt et al., 2007). The FWHM method is implemented as