research-article

Learning Motion Based Auxiliary Task for Cardiomyopathy Recognition with Cardiac Magnetic Resonance Images

Authors:

Jingjing Xiao,

Xiangjun Liu,

Qian Tao,

Jia ChenAuthors Info & Claims

CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering

Article No.: 137, Pages 1 - 5

https://doi.org/10.1145/3424978.3425122

Published: 20 October 2020 Publication History

Get Access

Abstract

Accurate analysis of the patient's heart function, and early diagnosis of myocardial disease can improve the treatment effect and reduce the medical cost significantly. Among the different medical imaging techniques, cardiac magnetic resonance (CMR) has high tissue contrast which is widely used in clinic. However, pro-processing CMR data manually for diagnose is extremely time consuming. To develop an automatic cardiomyopathy recognition algorithm among normal group, hypertrophic cardiomyopathy, and dilated cardiomyopathy group, we employ the CNN and LSTM to extract spatial and motion features. In addition, we propose a motion based auxiliary task to help the main recognition task, without additional annotation. In experiment, compared to C3D [1] and LRCN [2], the proposed method obtains the best performance. Both accuracy and AUC score achieve 0.94.

References

[1]

J Donahue, L A Hendricks, S Guadarrama, M Rohrbach, S Venugopalan, K Saenko and T Darrell (2015). Long-term recurrent convolutional networks for visual recognition and description[C]. In CVPR, 2625--2634.

Google Scholar

[2]

D Tran, L Bourdev, R Fergus, L Torresani and M Paluri (2015). Learning spatiotemporal features with 3D convolutional networks[C]. In ICCV, 4489--4497.

Google Scholar

[3]

M Roh'e, M Sermesant and X Pennec (2016). Barycentric subspace analysis: a new symmetric group-wise paradigm for cardiac motion tracking[C]. In MICCAI, 300--307.

Google Scholar

[4]

Malahfji M and Shah D J (2020). Cardiac Magnetic Resonance in Valvular Heart Disease: Assessment of Severity and Myocardial Remodeling. Methodist Debakey Cardiovasc[J]. 16(2).

Google Scholar

[5]

K Jafarian, V Vahdat, S Salehi, et al. (2020). Automating detection and localization of myocardial infarction using shallow and end-to-end deep neural networks[J]. Applied Soft Computing, 106383.

Google Scholar

[6]

P Claus, A M S Omar, G Pedrizzetti, P P Sengupta and E Nagel (2015). Tissue tracking technology for assessing cardiac mechanics: Principles, normal values, and clinical applications[J]. JACC: Cardiovascular Imaging, 8(12), 1444--1460.

Crossref

Google Scholar

[7]

Snel G J H, van den Boomen M, Hernandez L M, et al. (2020). Cardiovascular magnetic resonance native T2 and T2 quantitative values for cardiomyopathies and heart transplantations: a systematic review and meta-analysis[J]. Cardiovasc Magn Reson., 22(1), 34.

Crossref

Google Scholar

[8]

N Tajbakhsh, J Y Shin, S R Gurudu, R T Hurst, C B Kendall, M B Gotway and J Liang (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? [J]. IEEE Transactions on Medical Imaging, 35(5), 1299--1312.

Crossref

Google Scholar

[9]

N Strodthoff and C Strodthoff (2019). Detecting and interpreting myocardial infarction using fully convolutional neural networks[J]. Physiological Measurement, 40(1), 015001.

Crossref

Google Scholar

[10]

A Krizhevsky, I Sutskever and G E Hinton (2012). ImageNet classification with deep convolutional neural networks[C]. In NIPS, 1097--1105.

Google Scholar

[11]

Z Xiong, V Fedorov, X Fu, et al. (2019). Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network[J]. IEEE Transactions on Medical Imaging, 38(2), 515--524.

Crossref

Google Scholar

[12]

K Du, X Lin, Y Sun and X Ma (2019). CrossInfoNet: Multi-Task Information Sharing Based Hand Pose Estimation[C]. In CVPR, 9896--9905.

Google Scholar

[13]

D P Kingma and J Ba (2015). Adam: A Method for Stochastic Optimization[C]. ICLR.

Google Scholar

Cited By

View all

Ye QQiao LChen HTao QXiao J(2021)Automatic Cardiomyopathy Diagnosis with a Cost-sensitive Ensemble Classifier2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT53529.2021.9731304(775-779)Online publication date: 29-Oct-2021
https://doi.org/10.1109/ACAIT53529.2021.9731304

Index Terms

Learning Motion Based Auxiliary Task for Cardiomyopathy Recognition with Cardiac Magnetic Resonance Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

Recommendations

Evolving Diagnostic Role of Cardiac Magnetic Resonance in Acute Coronary Syndrome
A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance
A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists,...
Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
Abstract
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as ...
Highlights
- A comprehensive review of the diagnosis of CVDs from CMR images using DL techniques is provided.
- The main pros and cons of CMRI data for CVDs diagnosis are introduced.
- Most popular DL models in this field of study are presented.

Comments

Information & Contributors

Information

Published In

CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering

October 2020

1038 pages

ISBN:9781450377720

DOI:10.1145/3424978

Conference Chair:
Ali Emrouznejad,
Program Chair:
Jui-Sheng Rayson Chou

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CSAE 2020

CSAE 2020: The 4th International Conference on Computer Science and Application Engineering

October 20 - 22, 2020

Sanya, China

Acceptance Rates

CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;

Overall Acceptance Rate 368 of 770 submissions, 48%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
36
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ye QQiao LChen HTao QXiao J(2021)Automatic Cardiomyopathy Diagnosis with a Cost-sensitive Ensemble Classifier2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT53529.2021.9731304(775-779)Online publication date: 29-Oct-2021
https://doi.org/10.1109/ACAIT53529.2021.9731304

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Evolving Diagnostic Role of Cardiac Magnetic Resonance in Acute Coronary Syndrome

A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review