Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this work, we propose solutions for the two tasks of the CMRxMotion challenge; 1) quality control and 2) image segmentation in the presence of respiratory motion artifacts. We develop a k-space based motion simulation approach to generate cardiac MR images with respiratory motion artifacts on open-source artifact-free data to handle data scarcity. For task 1, a motion-denoising auto-encoder is trained to reconstruct motion-free images from the pairs of images with and without simulated motion. The encoder part of the auto-encoder is used as a feature extractor for a fully-connected classifier. For task 2, an ensemble of modified 2D nn-Unet models is proposed to tackle different aspects of variations in the data with the purpose of improving the robustness of the model to images hampered by respiratory motion artifacts. All proposed models in this paper are trained using the images with simulated motion artifacts. The proposed quality control model achieves a classification accuracy of 0.75 with the Cohen’s kappa coefficient of 0.64 and the ensemble model obtains the mean Dice scores of 0.922, 0.829, and 0.910 respectively for the left ventricle, myocardium, and right ventricle segmentation on the validation set of the CMRxMotion challenge.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers
Subtitle of host publication13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers
EditorsOscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang
Place of PublicationCham
PublisherSpringer
Chapter44
Pages466-475
Number of pages10
ISBN (Electronic)978-3-031-23443-9
ISBN (Print)978-3-031-23442-2
DOIs
Publication statusPublished - 2022
Event13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202218 Sept 2022

Publication series

NameLecture Notes in Computer Science (LNCS)
Volume13593
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/2218/09/22

Bibliographical note

Funding Information:
Acknowledgments. This research is a part of the openGTN project, supported by the European Union in the Marie Curie Innovative Training Networks (ITN) fellowship program under project No. 764465.

Keywords

  • Cardiac image segmentation
  • Motion artifact simulation
  • Quality control
  • Respiratory motion artifacts

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