Classification of myocardial infarcted patients by combining shape and motion features

Wenjia Bai; Ozan Oktay; Daniel Rueckert

doi:10.1007/978-3-319-28712-6_15

Classification of myocardial infarcted patients by combining shape and motion features

Wenjia Bai, Ozan Oktay, Daniel Rueckert

Imperial College London

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Myocardial infarction changes both the shape and motion of the heart. In this work, cardiac shape and motion features are extracted from shape models at ED and ES phases and combined to train a SVM classifier between myocardial infarcted cases and asymptomatic cases. Shape features are characterised by PCA coefficients of a shape model, whereas motion features include wall thickening and wall motion. Evaluated on the STACOM 2015 challenge dataset, the proposed method achieves a high accuracy of 97.5% for classification, which shows that shape and motion features can be useful biomarkers for myocardial infarction, which provide complementary information to late-gadolinium MR assessment.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart
Subtitle of host publication	Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
Editors	Kawal Rhode, Oscar Camara, Alistair Young, Tommaso Mansi, Maxime Sermesant, Mihaela Pop
Publisher	Springer Verlag
Pages	140-145
Number of pages	6
ISBN (Print)	9783319287119
DOIs	https://doi.org/10.1007/978-3-319-28712-6_15
State	Published - 2016
Externally published	Yes
Event	6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015 - Munich, Germany Duration: 9 Oct 2015 → 9 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9534
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015
Country/Territory	Germany
City	Munich
Period	9/10/15 → 9/10/15

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10.1007/978-3-319-28712-6_15

Cite this

Bai, W., Oktay, O., & Rueckert, D. (2016). Classification of myocardial infarcted patients by combining shape and motion features. In K. Rhode, O. Camara, A. Young, T. Mansi, M. Sermesant, & M. Pop (Eds.), Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 140-145). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9534). Springer Verlag. https://doi.org/10.1007/978-3-319-28712-6_15

Bai, Wenjia ; Oktay, Ozan ; Rueckert, Daniel. / Classification of myocardial infarcted patients by combining shape and motion features. Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. editor / Kawal Rhode ; Oscar Camara ; Alistair Young ; Tommaso Mansi ; Maxime Sermesant ; Mihaela Pop. Springer Verlag, 2016. pp. 140-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Myocardial infarction changes both the shape and motion of the heart. In this work, cardiac shape and motion features are extracted from shape models at ED and ES phases and combined to train a SVM classifier between myocardial infarcted cases and asymptomatic cases. Shape features are characterised by PCA coefficients of a shape model, whereas motion features include wall thickening and wall motion. Evaluated on the STACOM 2015 challenge dataset, the proposed method achieves a high accuracy of 97.5% for classification, which shows that shape and motion features can be useful biomarkers for myocardial infarction, which provide complementary information to late-gadolinium MR assessment.",

author = "Wenjia Bai and Ozan Oktay and Daniel Rueckert",

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year = "2016",

doi = "10.1007/978-3-319-28712-6_15",

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Bai, W, Oktay, O & Rueckert, D 2016, Classification of myocardial infarcted patients by combining shape and motion features. in K Rhode, O Camara, A Young, T Mansi, M Sermesant & M Pop (eds), Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9534, Springer Verlag, pp. 140-145, 6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015, Munich, Germany, 9/10/15. https://doi.org/10.1007/978-3-319-28712-6_15

Classification of myocardial infarcted patients by combining shape and motion features. / Bai, Wenjia; Oktay, Ozan; Rueckert, Daniel.
Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. ed. / Kawal Rhode; Oscar Camara; Alistair Young; Tommaso Mansi; Maxime Sermesant; Mihaela Pop. Springer Verlag, 2016. p. 140-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9534).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Oktay, Ozan

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N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

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N2 - Myocardial infarction changes both the shape and motion of the heart. In this work, cardiac shape and motion features are extracted from shape models at ED and ES phases and combined to train a SVM classifier between myocardial infarcted cases and asymptomatic cases. Shape features are characterised by PCA coefficients of a shape model, whereas motion features include wall thickening and wall motion. Evaluated on the STACOM 2015 challenge dataset, the proposed method achieves a high accuracy of 97.5% for classification, which shows that shape and motion features can be useful biomarkers for myocardial infarction, which provide complementary information to late-gadolinium MR assessment.

AB - Myocardial infarction changes both the shape and motion of the heart. In this work, cardiac shape and motion features are extracted from shape models at ED and ES phases and combined to train a SVM classifier between myocardial infarcted cases and asymptomatic cases. Shape features are characterised by PCA coefficients of a shape model, whereas motion features include wall thickening and wall motion. Evaluated on the STACOM 2015 challenge dataset, the proposed method achieves a high accuracy of 97.5% for classification, which shows that shape and motion features can be useful biomarkers for myocardial infarction, which provide complementary information to late-gadolinium MR assessment.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Statistical Atlases and Computational Models of the Heart

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Bai W, Oktay O, Rueckert D. Classification of myocardial infarcted patients by combining shape and motion features. In Rhode K, Camara O, Young A, Mansi T, Sermesant M, Pop M, editors, Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. Springer Verlag. 2016. p. 140-145. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-28712-6_15

Classification of myocardial infarcted patients by combining shape and motion features

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