MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation

Tianchen Wang; Jinjun Xiong; Xiaowei Xu; Meng Jiang; Haiyun Yuan; Meiping Huang; Jian Zhuang; Yiyu Shi

doi:10.1007/978-3-030-32245-8_68

MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation

Tianchen Wang
, Jinjun Xiong
, Xiaowei Xu
, Meng Jiang
, Haiyun Yuan
, Meiping Huang
, Jian Zhuang
, Yiyu Shi

Department of Computer Science and Engineering

University of Notre Dame
Guangdong General Hospital

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

33 Scopus citations

Abstract

Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention. The MRI videos are expected to be segmented on-the-fly in real practice. However, existing segmentation methods would suffer from drastic accuracy loss when modified for speedup. In this work, we propose Multiscale Statistical U-Net (MSU-Net) for real-time 3D MRI video segmentation in cardiac surgical guidance. Our idea is to model the input samples as multiscale canonical form distributions for speedup, while the spatio-temporal correlation is still fully utilized. A parallel statistical U-Net is then designed to efficiently process these distributions. The fast data sampling and efficient parallel structure of MSU-Net endorse the fast and accurate inference. Compared with vanilla U-Net and a modified state-of-the-art method GridNet, our method achieves up to 268% and 237% speedup with 1.6% and 3.6% increased Dice scores.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	614-622
Number of pages	9
ISBN (Print)	9783030322441
DOIs	https://doi.org/10.1007/978-3-030-32245-8_68
State	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: Oct 13 2019 → Oct 17 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11765 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	10/13/19 → 10/17/19

Access to Document

10.1007/978-3-030-32245-8_68

Cite this

Wang, T., Xiong, J., Xu, X., Jiang, M., Yuan, H., Huang, M., Zhuang, J., & Shi, Y. (2019). MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 614-622). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11765 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32245-8_68

Wang, Tianchen ; Xiong, Jinjun ; Xu, Xiaowei et al. / MSU-Net : Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 614-622 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8e9e4c192a4b43a6bb89044119076820,

title = "MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation",

abstract = "Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention. The MRI videos are expected to be segmented on-the-fly in real practice. However, existing segmentation methods would suffer from drastic accuracy loss when modified for speedup. In this work, we propose Multiscale Statistical U-Net (MSU-Net) for real-time 3D MRI video segmentation in cardiac surgical guidance. Our idea is to model the input samples as multiscale canonical form distributions for speedup, while the spatio-temporal correlation is still fully utilized. A parallel statistical U-Net is then designed to efficiently process these distributions. The fast data sampling and efficient parallel structure of MSU-Net endorse the fast and accurate inference. Compared with vanilla U-Net and a modified state-of-the-art method GridNet, our method achieves up to 268\% and 237\% speedup with 1.6\% and 3.6\% increased Dice scores.",

author = "Tianchen Wang and Jinjun Xiong and Xiaowei Xu and Meng Jiang and Haiyun Yuan and Meiping Huang and Jian Zhuang and Yiyu Shi",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

year = "2019",

doi = "10.1007/978-3-030-32245-8\_68",

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

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editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, \{Terry M.\} and Ali Khan and Staib, \{Lawrence H.\} and Caroline Essert and Sean Zhou",

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address = "Germany",

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Wang, T, Xiong, J, Xu, X, Jiang, M, Yuan, H, Huang, M, Zhuang, J & Shi, Y 2019, MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11765 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 614-622, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 10/13/19. https://doi.org/10.1007/978-3-030-32245-8_68

MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation. / Wang, Tianchen; Xiong, Jinjun; Xu, Xiaowei et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 614-622 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11765 LNCS).

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

TY - GEN

T1 - MSU-Net

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

AU - Wang, Tianchen

AU - Xiong, Jinjun

AU - Xu, Xiaowei

AU - Jiang, Meng

AU - Yuan, Haiyun

AU - Huang, Meiping

AU - Zhuang, Jian

AU - Shi, Yiyu

PY - 2019

Y1 - 2019

N2 - Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention. The MRI videos are expected to be segmented on-the-fly in real practice. However, existing segmentation methods would suffer from drastic accuracy loss when modified for speedup. In this work, we propose Multiscale Statistical U-Net (MSU-Net) for real-time 3D MRI video segmentation in cardiac surgical guidance. Our idea is to model the input samples as multiscale canonical form distributions for speedup, while the spatio-temporal correlation is still fully utilized. A parallel statistical U-Net is then designed to efficiently process these distributions. The fast data sampling and efficient parallel structure of MSU-Net endorse the fast and accurate inference. Compared with vanilla U-Net and a modified state-of-the-art method GridNet, our method achieves up to 268% and 237% speedup with 1.6% and 3.6% increased Dice scores.

AB - Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention. The MRI videos are expected to be segmented on-the-fly in real practice. However, existing segmentation methods would suffer from drastic accuracy loss when modified for speedup. In this work, we propose Multiscale Statistical U-Net (MSU-Net) for real-time 3D MRI video segmentation in cardiac surgical guidance. Our idea is to model the input samples as multiscale canonical form distributions for speedup, while the spatio-temporal correlation is still fully utilized. A parallel statistical U-Net is then designed to efficiently process these distributions. The fast data sampling and efficient parallel structure of MSU-Net endorse the fast and accurate inference. Compared with vanilla U-Net and a modified state-of-the-art method GridNet, our method achieves up to 268% and 237% speedup with 1.6% and 3.6% increased Dice scores.

UR - https://www.scopus.com/pages/publications/85075670262

U2 - 10.1007/978-3-030-32245-8_68

DO - 10.1007/978-3-030-32245-8_68

M3 - Conference contribution

AN - SCOPUS:85075670262

SN - 9783030322441

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

SP - 614

EP - 622

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 13 October 2019 through 17 October 2019

ER -

Wang T, Xiong J, Xu X, Jiang M, Yuan H, Huang M et al. MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 614-622. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32245-8_68

MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation

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