DBAN : Adversarial Network with Multi-Scale Features for Cardiac MRI Segmentation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Xinyu Yang
  • Yuan Zhang
  • Benny Lo
  • Dongrui Wu
  • Hongen Liao

Detail(s)

Original languageEnglish
Pages (from-to)2018-2028
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number6
Online published2 Oct 2020
Publication statusPublished - Jun 2021

Abstract

With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator. In the segmentor, the dilated block (DB) is proposed to capture, and aggregate multi-scale features. The segmentor can produce segmentation probability maps while the discriminator can differentiate the segmentation probability map, and the ground truth at the pixel level. In addition, confidence probability maps generated by the discriminator can guide the segmentor to modify segmentation probability maps. Extensive experiments demonstrate that DBAN has achieved the state-of-the-art performance on the ACDC dataset. Quantitative analyses indicate that cardiac function indices from DBAN are similar to those from clinical experts. Therefore, DBAN can be a potential candidate for short-axis cardiac MRI segmentation in clinical applications.

Research Area(s)

  • Adversarial Network, Automatic Segmentation Method, Cardiac MRI, Medical Image Processing

Citation Format(s)

DBAN : Adversarial Network with Multi-Scale Features for Cardiac MRI Segmentation. / Yang, Xinyu; Zhang, Yuan; Lo, Benny; Wu, Dongrui; Liao, Hongen; Zhang, Yuan-Ting.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 25, No. 6, 06.2021, p. 2018-2028.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review