Publications/zhao.20.icpr.1

Authors

Zhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud

Where

Proceedings of the 25th International Conference on Pattern Recognition (ICPR)

Type

inproceedings

Projects

Olena

Keywords

Image

Date

2020-11-02

Abstract

In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization stepa Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a three-level hierarchy (pixel-patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries. We demonstrate the efficiency of our approach on two public datasets in terms of regional and boundary segmentations.

Documents

• Paper

Bibtex (lrde.bib)

@InProceedings{	  zhao.20.icpr.1,
  author	= {Zhou Zhao and Nicolas Boutry and \'Elodie Puybareau and
		  Thierry G\'eraud},
  title		= {{FOANet}: {A} Focus of Attention Network with Application
		  to Myocardium Segmentation},
  booktitle	= {Proceedings of the 25th International Conference on
		  Pattern Recognition (ICPR)},
  year		= 2020,
  abstract	= {In myocardium segmentation of cardiac magnetic resonance
		  images, ambiguities often appear near the boundaries of the
		  target domains due to tissue similarities. To address this
		  issue, we propose a new architecture, called FOANet, which
		  can be decomposed in three main steps: a localization step,
		  a Gaussian-based contrast enhancement step, and a
		  segmentation step. This architecture is supplied with a
		  hybrid loss function that guides the FOANet to study the
		  transformation relationship between the input image and the
		  corresponding label in a three-level hierarchy (pixel-,
		  patch- and map-level), which is helpful to improve
		  segmentation and recovery of the boundaries. We demonstrate
		  the efficiency of our approach on two public datasets in
		  terms of regional and boundary segmentations.}
}