ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation

Tianchen Wang; Xiaowei Xu; Jinjun Xiong; Qianjun Jia; Haiyun Yuan; Meiping Huang; Jian Zhuang; Yiyu Shi

doi:10.1007/978-3-030-59725-2_43

ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation

Tianchen Wang
, Xiaowei Xu
, Jinjun Xiong
, Qianjun Jia
, 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

23 Scopus citations

Abstract

Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis (ICA)[11] for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation. Experiments using the MICCAI ACDC 2017 dataset show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6× reduction), enabling real-time guidance for cardiac interventions without visual lag.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	447-457
Number of pages	11
ISBN (Print)	9783030597245
DOIs	https://doi.org/10.1007/978-3-030-59725-2_43
State	Published - 2020
Event	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: Oct 4 2020 → Oct 8 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12266 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	10/4/20 → 10/8/20

Access to Document

10.1007/978-3-030-59725-2_43

Cite this

Wang, T., Xu, X., Xiong, J., Jia, Q., Yuan, H., Huang, M., Zhuang, J., & Shi, Y. (2020). ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (pp. 447-457). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12266 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59725-2_43

Wang, Tianchen ; Xu, Xiaowei ; Xiong, Jinjun et al. / ICA-UNet : ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 447-457 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7acca4cd0d6e4042b6351e3cee71b75f,

title = "ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation",

abstract = "Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis (ICA)[11] for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation. Experiments using the MICCAI ACDC 2017 dataset show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6{\texttimes} reduction), enabling real-time guidance for cardiac interventions without visual lag.",

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

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

year = "2020",

doi = "10.1007/978-3-030-59725-2\_43",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "447--457",

editor = "Martel, \{Anne L.\} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, \{Maria A.\} and Zhou, \{S. Kevin\} and Daniel Racoceanu and Leo Joskowicz",

booktitle = "Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2020 - 23rd International Conference, Proceedings",

address = "Germany",

}

Wang, T, Xu, X, Xiong, J, Jia, Q, Yuan, H, Huang, M, Zhuang, J & Shi, Y 2020, ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12266 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 447-457, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 10/4/20. https://doi.org/10.1007/978-3-030-59725-2_43

ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation. / Wang, Tianchen; Xu, Xiaowei; Xiong, Jinjun et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. p. 447-457 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12266 LNCS).

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

TY - GEN

T1 - ICA-UNet

T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

AU - Wang, Tianchen

AU - Xu, Xiaowei

AU - Xiong, Jinjun

AU - Jia, Qianjun

AU - Yuan, Haiyun

AU - Huang, Meiping

AU - Zhuang, Jian

AU - Shi, Yiyu

PY - 2020

Y1 - 2020

N2 - Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis (ICA)[11] for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation. Experiments using the MICCAI ACDC 2017 dataset show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6× reduction), enabling real-time guidance for cardiac interventions without visual lag.

AB - Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis (ICA)[11] for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation. Experiments using the MICCAI ACDC 2017 dataset show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6× reduction), enabling real-time guidance for cardiac interventions without visual lag.

UR - https://www.scopus.com/pages/publications/85092797765

U2 - 10.1007/978-3-030-59725-2_43

DO - 10.1007/978-3-030-59725-2_43

M3 - Conference contribution

AN - SCOPUS:85092797765

SN - 9783030597245

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

SP - 447

EP - 457

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings

A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

A2 - Stoyanov, Danail

A2 - Mateus, Diana

A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

A2 - Joskowicz, Leo

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 4 October 2020 through 8 October 2020

ER -

Wang T, Xu X, Xiong J, Jia Q, Yuan H, Huang M et al. ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 447-457. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59725-2_43

ICA-UNet: ICA Inspired Statistical UNet for Real-Time 3D Cardiac Cine MRI Segmentation

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