Supervised learning of functional maps for infarct classification

Anirban Mukhopadhyay; Ilkay Oksuz; Sotirios A. Tsaftaris

doi:10.1007/978-3-319-28712-6_18

Supervised learning of functional maps for infarct classification

Anirban Mukhopadhyay, Ilkay Oksuz^*, Sotirios A. Tsaftaris

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Our submission to the STACOM Challenge at MICCAI 2015 is based on the supervised learning of functional map representation between End Systole (ES) and End Diastole (ED) phases of Left Ventricle (LV), for classifying infarcted LV from the healthy ones. The Laplace- Beltrami eigen-spectrum of the LV surfaces at ES and ED, represented by their triangular meshes, are used to compute the functional maps. Multi-scale distortions induced by the mapping, are further calculated by singular value decomposition of the functional map. During training, the information of whether an LV surface is healthy or diseased is known, and this information is used to train an SVM classifier for the singular values at multiple scales corresponding to the distorted areas augmented with surface area difference of epicardium and endocardium meshes. At testing similar augmented features are calculated and fed to the SVM model for classification. Promising results are obtained on both cross validation of training data as well as on testing data, which encourages us in believing that this algorithm will perform favourably in comparison to state of the art methods.

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	162-170
Number of pages	9
ISBN (Print)	9783319287119
DOIs	https://doi.org/10.1007/978-3-319-28712-6_18
Publication status	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

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Cardiac remodelling
Infarct
Laplace-beltrami
SVD
SVM

Access to Document

10.1007/978-3-319-28712-6_18

Cite this

Mukhopadhyay, A., Oksuz, I., & Tsaftaris, S. A. (2016). Supervised learning of functional maps for infarct classification. 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. 162-170). (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_18

Mukhopadhyay, Anirban ; Oksuz, Ilkay ; Tsaftaris, Sotirios A. / Supervised learning of functional maps for infarct classification. 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. 162-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5289627b0a4e46c98871e305f2492c96,

title = "Supervised learning of functional maps for infarct classification",

abstract = "Our submission to the STACOM Challenge at MICCAI 2015 is based on the supervised learning of functional map representation between End Systole (ES) and End Diastole (ED) phases of Left Ventricle (LV), for classifying infarcted LV from the healthy ones. The Laplace- Beltrami eigen-spectrum of the LV surfaces at ES and ED, represented by their triangular meshes, are used to compute the functional maps. Multi-scale distortions induced by the mapping, are further calculated by singular value decomposition of the functional map. During training, the information of whether an LV surface is healthy or diseased is known, and this information is used to train an SVM classifier for the singular values at multiple scales corresponding to the distorted areas augmented with surface area difference of epicardium and endocardium meshes. At testing similar augmented features are calculated and fed to the SVM model for classification. Promising results are obtained on both cross validation of training data as well as on testing data, which encourages us in believing that this algorithm will perform favourably in comparison to state of the art methods.",

keywords = "Cardiac remodelling, Infarct, Laplace-beltrami, SVD, SVM",

author = "Anirban Mukhopadhyay and Ilkay Oksuz and Tsaftaris, {Sotirios A.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015 ; Conference date: 09-10-2015 Through 09-10-2015",

year = "2016",

doi = "10.1007/978-3-319-28712-6_18",

language = "English",

isbn = "9783319287119",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "162--170",

editor = "Kawal Rhode and Oscar Camara and Alistair Young and Tommaso Mansi and Maxime Sermesant and Mihaela Pop",

booktitle = "Statistical Atlases and Computational Models of the Heart",

address = "Germany",

}

Mukhopadhyay, A, Oksuz, I & Tsaftaris, SA 2016, Supervised learning of functional maps for infarct classification. 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. 162-170, 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_18

Supervised learning of functional maps for infarct classification. / Mukhopadhyay, Anirban; Oksuz, Ilkay; Tsaftaris, Sotirios A.
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. 162-170 (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

TY - GEN

T1 - Supervised learning of functional maps for infarct classification

AU - Mukhopadhyay, Anirban

AU - Oksuz, Ilkay

AU - Tsaftaris, Sotirios A.

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

PY - 2016

Y1 - 2016

N2 - Our submission to the STACOM Challenge at MICCAI 2015 is based on the supervised learning of functional map representation between End Systole (ES) and End Diastole (ED) phases of Left Ventricle (LV), for classifying infarcted LV from the healthy ones. The Laplace- Beltrami eigen-spectrum of the LV surfaces at ES and ED, represented by their triangular meshes, are used to compute the functional maps. Multi-scale distortions induced by the mapping, are further calculated by singular value decomposition of the functional map. During training, the information of whether an LV surface is healthy or diseased is known, and this information is used to train an SVM classifier for the singular values at multiple scales corresponding to the distorted areas augmented with surface area difference of epicardium and endocardium meshes. At testing similar augmented features are calculated and fed to the SVM model for classification. Promising results are obtained on both cross validation of training data as well as on testing data, which encourages us in believing that this algorithm will perform favourably in comparison to state of the art methods.

AB - Our submission to the STACOM Challenge at MICCAI 2015 is based on the supervised learning of functional map representation between End Systole (ES) and End Diastole (ED) phases of Left Ventricle (LV), for classifying infarcted LV from the healthy ones. The Laplace- Beltrami eigen-spectrum of the LV surfaces at ES and ED, represented by their triangular meshes, are used to compute the functional maps. Multi-scale distortions induced by the mapping, are further calculated by singular value decomposition of the functional map. During training, the information of whether an LV surface is healthy or diseased is known, and this information is used to train an SVM classifier for the singular values at multiple scales corresponding to the distorted areas augmented with surface area difference of epicardium and endocardium meshes. At testing similar augmented features are calculated and fed to the SVM model for classification. Promising results are obtained on both cross validation of training data as well as on testing data, which encourages us in believing that this algorithm will perform favourably in comparison to state of the art methods.

KW - Cardiac remodelling

KW - Infarct

KW - Laplace-beltrami

KW - SVD

KW - SVM

UR - http://www.scopus.com/inward/record.url?scp=84955247794&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-28712-6_18

DO - 10.1007/978-3-319-28712-6_18

M3 - Conference contribution

AN - SCOPUS:84955247794

SN - 9783319287119

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

SP - 162

EP - 170

BT - Statistical Atlases and Computational Models of the Heart

A2 - Rhode, Kawal

A2 - Camara, Oscar

A2 - Young, Alistair

A2 - Mansi, Tommaso

A2 - Sermesant, Maxime

A2 - Pop, Mihaela

PB - Springer Verlag

T2 - 6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015

Y2 - 9 October 2015 through 9 October 2015

ER -

Mukhopadhyay A, Oksuz I, Tsaftaris SA. Supervised learning of functional maps for infarct classification. 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. 162-170. (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_18

Supervised learning of functional maps for infarct classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this