Joint motion estimation and segmentation from undersampled cardiac mr image

Chen Qin; Wenjia Bai; Jo Schlemper; Steffen E. Petersen; Stefan K. Piechnik; Stefan Neubauer; Daniel Rueckert

doi:10.1007/978-3-030-00129-2_7

Joint motion estimation and segmentation from undersampled cardiac mr image

Chen Qin
, Wenjia Bai
, Jo Schlemper
, Steffen E. Petersen
, Stefan K. Piechnik
, Stefan Neubauer
, Daniel Rueckert

Imperial College London
Queen Mary University of London
University of Oxford

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

28 Scopus citations

Abstract

Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage.

Original language	English
Title of host publication	Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Florian Knoll, Andreas Maier, Daniel Rueckert
Publisher	Springer Verlag
Pages	55-63
Number of pages	9
ISBN (Print)	9783030001285
DOIs	https://doi.org/10.1007/978-3-030-00129-2_7
State	Published - 2018
Externally published	Yes
Event	1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sep 2018 → 16 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11074 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-030-00129-2_7

Cite this

Qin, C., Bai, W., Schlemper, J., Petersen, S. E., Piechnik, S. K., Neubauer, S., & Rueckert, D. (2018). Joint motion estimation and segmentation from undersampled cardiac mr image. In F. Knoll, A. Maier, & D. Rueckert (Eds.), Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 55-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11074 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00129-2_7

Qin, Chen ; Bai, Wenjia ; Schlemper, Jo et al. / Joint motion estimation and segmentation from undersampled cardiac mr image. Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Florian Knoll ; Andreas Maier ; Daniel Rueckert. Springer Verlag, 2018. pp. 55-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{674c4a20753c4544bf2b720a6fc26f6a,

title = "Joint motion estimation and segmentation from undersampled cardiac mr image",

abstract = "Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage.",

author = "Chen Qin and Wenjia Bai and Jo Schlemper and Petersen, \{Steffen E.\} and Piechnik, \{Stefan K.\} and Stefan Neubauer and Daniel Rueckert",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",

year = "2018",

doi = "10.1007/978-3-030-00129-2\_7",

language = "English",

isbn = "9783030001285",

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

publisher = "Springer Verlag",

pages = "55--63",

editor = "Florian Knoll and Andreas Maier and Daniel Rueckert",

booktitle = "Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings",

}

Qin, C, Bai, W, Schlemper, J, Petersen, SE, Piechnik, SK, Neubauer, S & Rueckert, D 2018, Joint motion estimation and segmentation from undersampled cardiac mr image. in F Knoll, A Maier & D Rueckert (eds), Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11074 LNCS, Springer Verlag, pp. 55-63, 1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00129-2_7

Joint motion estimation and segmentation from undersampled cardiac mr image. / Qin, Chen; Bai, Wenjia; Schlemper, Jo et al.
Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Florian Knoll; Andreas Maier; Daniel Rueckert. Springer Verlag, 2018. p. 55-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11074 LNCS).

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

TY - GEN

T1 - Joint motion estimation and segmentation from undersampled cardiac mr image

AU - Qin, Chen

AU - Bai, Wenjia

AU - Schlemper, Jo

AU - Petersen, Steffen E.

AU - Piechnik, Stefan K.

AU - Neubauer, Stefan

AU - Rueckert, Daniel

PY - 2018

Y1 - 2018

N2 - Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage.

AB - Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage.

UR - https://www.scopus.com/pages/publications/85053931446

U2 - 10.1007/978-3-030-00129-2_7

DO - 10.1007/978-3-030-00129-2_7

M3 - Conference contribution

AN - SCOPUS:85053931446

SN - 9783030001285

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

SP - 55

EP - 63

BT - Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings

A2 - Knoll, Florian

A2 - Maier, Andreas

A2 - Rueckert, Daniel

PB - Springer Verlag

T2 - 1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

Y2 - 16 September 2018 through 16 September 2018

ER -

Qin C, Bai W, Schlemper J, Petersen SE, Piechnik SK, Neubauer S et al. Joint motion estimation and segmentation from undersampled cardiac mr image. In Knoll F, Maier A, Rueckert D, editors, Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 55-63. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00129-2_7

Joint motion estimation and segmentation from undersampled cardiac mr image

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