Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training

Huaqi Qiu; Chen Qin; Loic Le Folgoc; Benjamin Hou; Jo Schlemper; Daniel Rueckert

doi:10.1007/978-3-030-39074-7_20

Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training

Huaqi Qiu, Chen Qin, Loic Le Folgoc, Benjamin Hou, Jo Schlemper, Daniel Rueckert

Imperial College London

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

Abstract

Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
Editors	Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
Publisher	Springer
Pages	186-194
Number of pages	9
ISBN (Print)	9783030390730
DOIs	https://doi.org/10.1007/978-3-030-39074-7_20
Publication status	Published - 1 Jan 2020
Externally published	Yes
Event	10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 13 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12009 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/19 → 13/10/19

Access to Document

10.1007/978-3-030-39074-7_20

Cite this

Qiu, H., Qin, C., Le Folgoc, L., Hou, B., Schlemper, J., & Rueckert, D. (2020). Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. In M. Pop, M. Sermesant, O. Camara, X. Zhuang, S. Li, A. Young, T. Mansi, & A. Suinesiaputra (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers (pp. 186-194). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12009 LNCS). Springer. https://doi.org/10.1007/978-3-030-39074-7_20

Qiu, Huaqi ; Qin, Chen ; Le Folgoc, Loic et al. / Deep Learning for Cardiac Motion Estimation : Supervised vs. Unsupervised Training. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. editor / Mihaela Pop ; Maxime Sermesant ; Oscar Camara ; Xiahai Zhuang ; Shuo Li ; Alistair Young ; Tommaso Mansi ; Avan Suinesiaputra. Springer, 2020. pp. 186-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3315fb328f8a464092e61900c542be9e,

title = "Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training",

abstract = "Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.",

author = "Huaqi Qiu and Chen Qin and \{Le Folgoc\}, Loic and Benjamin Hou and Jo Schlemper and Daniel Rueckert",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 13-10-2019",

year = "2020",

month = jan,

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doi = "10.1007/978-3-030-39074-7\_20",

language = "English",

isbn = "9783030390730",

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

publisher = "Springer",

pages = "186--194",

editor = "Mihaela Pop and Maxime Sermesant and Oscar Camara and Xiahai Zhuang and Shuo Li and Alistair Young and Tommaso Mansi and Avan Suinesiaputra",

booktitle = "Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers",

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Qiu, H, Qin, C, Le Folgoc, L, Hou, B, Schlemper, J & Rueckert, D 2020, Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. in M Pop, M Sermesant, O Camara, X Zhuang, S Li, A Young, T Mansi & A Suinesiaputra (eds), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12009 LNCS, Springer, pp. 186-194, 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-39074-7_20

Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. / Qiu, Huaqi; Qin, Chen; Le Folgoc, Loic et al.
Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. ed. / Mihaela Pop; Maxime Sermesant; Oscar Camara; Xiahai Zhuang; Shuo Li; Alistair Young; Tommaso Mansi; Avan Suinesiaputra. Springer, 2020. p. 186-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12009 LNCS).

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

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AU - Qiu, Huaqi

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AU - Hou, Benjamin

AU - Schlemper, Jo

AU - Rueckert, Daniel

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

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AB - Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.

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A2 - Pop, Mihaela

A2 - Sermesant, Maxime

A2 - Camara, Oscar

A2 - Zhuang, Xiahai

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Qiu H, Qin C, Le Folgoc L, Hou B, Schlemper J, Rueckert D. Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. In Pop M, Sermesant M, Camara O, Zhuang X, Li S, Young A, Mansi T, Suinesiaputra A, editors, Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Springer. 2020. p. 186-194. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39074-7_20

Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training

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