TY - JOUR A1 - Suinesiaputra, Avan A1 - Albin, Pierre A1 - Alba, Xenia A1 - Alessandrini, Martino A1 - Allen, Jack A1 - Bai, Wenjia A1 - Cimen, Serkan A1 - Claes, Peter A1 - Cowan, Brett A1 - D'hooge, Jan A1 - Duchateau, Nicolas A1 - Ehrhardt, Jan A1 - Frangi, Alejandro A1 - Gooya, Ali A1 - Grau, Vicente A1 - Lekadir, Karim A1 - Lu, Allen A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Parajuli, Nripesh A1 - Pennec, Xavier A1 - Pereanez, Marco A1 - Pinto, Catarina A1 - Piras, Paolo A1 - Rohe, Marc-Michael A1 - Rueckert, Daniel A1 - Saring, Dennis A1 - Sermesant, Maxime A1 - Siddiqi, Kaleem A1 - Tabassian, Mahdi A1 - Teresi, Lusiano A1 - Tsaftaris, Sotirios A1 - Wilms, Matthias A1 - Young, Alistair A1 - Zhang, Xingyu A1 - Medrano-Gracia, Pau T1 - Statistical shape modeling of the left ventricle: myocardial infarct classification challenge T2 - IEEE Journal of Biomedical and Health Informatics N2 - Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1. Y1 - 2017 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6251 IS - 99 ER -