Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge
Open accessAbstract
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.
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2018 Index IEEE Journal of Biomedical and Health Informatics Vol. 22
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- Authors
- Suinesiaputra, A., Dhooge, J., Duchateau, N., Ehrhardt, J., Frangi, A. F., Gooya, A., Grau, V., Lekadir, K., Lu, A., Mukhopadhyay, A., Oksuz, I., Ablin, P., Parajuli, N., Pennec, X., Pereanez, M., Pinto, C., Piras, P., Rohe, M.-M., Rueckert, D., Saring, D., Sermesant, M., Siddiqi, K., Alba, X., Tabassian, M., Teresi, L., Tsaftaris, S. A., Wilms, M., Young, A. A., Zhang, X., Medrano-Gracia, P., Alessandrini, M., Allen, J., Bai, W., Cimen, S., Claes, P., & Cowan, B. R.
- Affiliations
- University of Auckland (NZ)
- University of Lübeck (DE)
- University of Sheffield (GB)
- University of Oxford (GB)
- Universitat Pompeu Fabra (ES)
- Yale University (US)
- Zuse Institute Berlin (DE)
- McGill University (CA)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Sapienza University of Rome (IT)
- Imperial College London (GB)
- Universitair Ziekenhuis Leuven (BE)
- Roma Tre University (IT)
- IMT School for Advanced Studies Lucca (IT)
- KU Leuven (BE)
- ORCID
- Suinesiaputra, Avan
- Ehrhardt, Jan
- Frangi, Alejandro F.
- Gooya, Ali
- Grau, Vicente
- Lekadir, Karim
- Ablin, Pierre
- Parajuli, Nripesh
- Pennec, Xavier
- Piras, Paolo
- Rueckert, Daniel
- Tabassian, Mahdi
- Teresi, Luciano
- Tsaftaris, Sotirios A.
- Wilms, Matthias
- Young, Alistair A.
- Alessandrini, Martino
- Allen, Jack
- Bai, Wenjia
- Claes, Peter
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2018-03
- Volume
- 22
- Issue
- 2
- Pages
- 503-515
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- PMC
- PMC5857476
- OpenAlex
- W2575820504
- Cited by
- 95
- References
- 70
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 9 · 2023: 11 · 2022: 16 · 2021: 15 · 2020: 9 · 2019: 6 · 2018: 7 · 2017: 6 · 2016: 1
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Lirias (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- ResearchSpace (University of Auckland) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL087773, R01HL121754)
- Auckland Medical Research Foundation (1114006)
- Medical Research Council and Engineering and Physical Sciences Research Council (EP/L016052/1)
- BBSRC (BB/I012117/1)
- BHF New Horizon Grant (NH/13/30238)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- statistical shape models, left ventricle, myocardial infarction, left ventricular remodeling
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