High-quality segmentation of low quality cardiac MR images using k-space artefact correction

Ilkay Oksuz, James Clough, Wenjia Bai, Bram Ruijsink, Esther Puyol-Antón, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A. Schnabel

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

Deep learning methods have shown great success in segmenting the anatomical and pathological structures in medical images. This success is closely bounded with the quality of the images in the dataset that are being segmented. A commonly overlooked issue in the medical image analysis community is the vast amount of clinical images that have severe image artefacts. In this paper, we discuss the implications of image artefacts on cardiac MR segmentation and compare a variety of approaches for motion artefact correction with our proposed method Automap-GAN. Our method is based on the recently developed Automap reconstruction method, which directly reconstructs high quality MR images from k-space using deep learning. We propose to use a loss function that combines mean square error with structural similarity index to robustly segment poor-quality images. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted CMR k-space data and uncorrected reconstructed images. In the experiments, we apply the proposed method to correct for motion artefacts on a large dataset of 1,400 subjects to improve image quality. The improvement of image quality is quantitatively assessed using segmentation accuracy as a metric. The segmentation is improved from 0.63 to 0.72 dice overlap after artefact correction. We quantitatively compare our method with a variety of techniques for recovering image quality to showcase the influence on segmentation. In addition, we qualitatively evaluate the proposed technique using k-space data containing real motion artefacts.

Original languageEnglish
Pages (from-to)380-389
Number of pages10
JournalProceedings of Machine Learning Research
Volume102
Publication statusPublished - 2019
Externally publishedYes
Event2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019 - London, United Kingdom
Duration: 8 Jul 201910 Jul 2019

Bibliographical note

Publisher Copyright:
© 2019 I. Oksuz, J. Clough, W. Bai, B. Ruijsink, E. Puyol-Antón, G. Cruz, C. Prieto, A.P. King & J.A. Schnabel.

Keywords

  • Automap
  • Cardiac MR Segmentation
  • Deep Learning
  • Image Artefact Correction
  • Image Artefacts
  • Image Quality
  • UK Biobank

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