Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension

Jinming Duan; Jo Schlemper; Wenjia Bai; Timothy J.W. Dawes; Ghalib Bello; Georgia Doumou; Antonio De Marvao; Declan P. O’Regan; Daniel Rueckert

doi:10.1007/978-3-030-00937-3_68

Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension

Jinming Duan, Jo Schlemper, Wenjia Bai, Timothy J.W. Dawes, Ghalib Bello, Georgia Doumou, Antonio De Marvao, Declan P. O’Regan, Daniel Rueckert

Imperial College London

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

23 Scopus citations

Abstract

In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
Editors	Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos
Publisher	Springer Verlag
Pages	595-603
Number of pages	9
ISBN (Print)	9783030009366
DOIs	https://doi.org/10.1007/978-3-030-00937-3_68
State	Published - 2018
Externally published	Yes
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sep 2018 → 20 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11073 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 20/09/18

Access to Document

10.1007/978-3-030-00937-3_68

Cite this

Duan, J., Schlemper, J., Bai, W., Dawes, T. J. W., Bello, G., Doumou, G., De Marvao, A., O’Regan, D. P., & Rueckert, D. (2018). Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension. In A. F. Frangi, G. Fichtinger, J. A. Schnabel, C. Alberola-López, & C. Davatzikos (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 595-603). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11073 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_68

Duan, Jinming ; Schlemper, Jo ; Bai, Wenjia et al. / Deep Nested Level Sets : Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. editor / Alejandro F. Frangi ; Gabor Fichtinger ; Julia A. Schnabel ; Carlos Alberola-López ; Christos Davatzikos. Springer Verlag, 2018. pp. 595-603 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cd1114738835400fa01db03fa7e51360,

title = "Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension",

abstract = "In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.",

author = "Jinming Duan and Jo Schlemper and Wenjia Bai and Dawes, \{Timothy J.W.\} and Ghalib Bello and Georgia Doumou and \{De Marvao\}, Antonio and O{\textquoteright}Regan, \{Declan P.\} and Daniel Rueckert",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",

year = "2018",

doi = "10.1007/978-3-030-00937-3\_68",

language = "English",

isbn = "9783030009366",

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

publisher = "Springer Verlag",

pages = "595--603",

editor = "Frangi, \{Alejandro F.\} and Gabor Fichtinger and Schnabel, \{Julia A.\} and Carlos Alberola-L{\'o}pez and Christos Davatzikos",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings",

}

Duan, J, Schlemper, J, Bai, W, Dawes, TJW, Bello, G, Doumou, G, De Marvao, A, O’Regan, DP & Rueckert, D 2018, Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension. in AF Frangi, G Fichtinger, JA Schnabel, C Alberola-López & C Davatzikos (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11073 LNCS, Springer Verlag, pp. 595-603, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00937-3_68

Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension. / Duan, Jinming; Schlemper, Jo; Bai, Wenjia et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. ed. / Alejandro F. Frangi; Gabor Fichtinger; Julia A. Schnabel; Carlos Alberola-López; Christos Davatzikos. Springer Verlag, 2018. p. 595-603 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11073 LNCS).

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

TY - GEN

T1 - Deep Nested Level Sets

T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

AU - Duan, Jinming

AU - Schlemper, Jo

AU - Bai, Wenjia

AU - Dawes, Timothy J.W.

AU - Bello, Ghalib

AU - Doumou, Georgia

AU - De Marvao, Antonio

AU - O’Regan, Declan P.

AU - Rueckert, Daniel

PY - 2018

Y1 - 2018

N2 - In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.

AB - In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.

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SN - 9783030009366

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

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings

A2 - Frangi, Alejandro F.

A2 - Fichtinger, Gabor

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A2 - Alberola-López, Carlos

A2 - Davatzikos, Christos

PB - Springer Verlag

Y2 - 16 September 2018 through 20 September 2018

ER -

Duan J, Schlemper J, Bai W, Dawes TJW, Bello G, Doumou G et al. Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension. In Frangi AF, Fichtinger G, Schnabel JA, Alberola-López C, Davatzikos C, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 595-603. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00937-3_68

Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension

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