Fully automatic segmentation of short-axis cardiac MRI using modified deep layer aggregation

Zhongyu Li, Yixuan Lou, Zhennan Yan, Subhi Alraref, James K. Min, Leon Axel, Dimitris N. Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Delineation of right ventricular cavity (RVC), left ventricular myocardium (LVM) and left ventricular cavity (LVC) are common tasks in the clinical diagnosis of cardiac related diseases, especially in the basis of advanced magnetic resonance imaging (MRI) techniques. Recently, despite deep learning techniques being widely employed in solving segmentation tasks in a variety of medical images, the sheer volume and complexity of the data in some applications such as cine cardiac MRI pose significant challenges for the accurate and efficient segmentation. In cine cardiac MRI we need to segment both short and long axis 2D images. In this paper, we focus on the automated segmentation of short-axis cardiac MRI images. We first introduce the deep layer aggregation (DLA) method to augment the standard deep learning architecture with deeper aggregation to better fuse information across layers, which is particularly suitable for the cardiac MRI segmentation, due to the complexity of the cardiac boundaries appearance and acquisition resolution during a cardiac cycle. In our solution, we develop a modified DLA framework by embedding Refinement Residual Block (RRB) and Channel Attention Block (CAB). Experimental results validate the superior performance of our proposed method for the cardiac structures segmentation in comparison with state-of-the-art. Moreover, we demonstrate its potential use case in the quantitative analysis of cardiac dyssynchrony.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages793-797
Number of pages5
ISBN (Electronic)9781538636411
DOIs
StatePublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period4/8/194/11/19

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Cardiac MRI
  • Deep learning
  • Image segmentation
  • Left and right ventricles

Fingerprint

Dive into the research topics of 'Fully automatic segmentation of short-axis cardiac MRI using modified deep layer aggregation'. Together they form a unique fingerprint.

Cite this