Skip to Main content Skip to Navigation
New interface
Preprints, Working Papers, ...

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

Abstract : In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Ivan PORCHEROT Connect in order to contact the contributor
Submitted on : Wednesday, May 16, 2018 - 3:31:09 PM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM

Links full text


  • HAL Id : hal-01793441, version 1
  • ARXIV : 1705.08943


Clement Zotti, Zhiming Luo, Alain Lalande, Olivier Humbert, Pierre-Marc Jodoin. GridNet with automatic shape prior registration for automatic MRI cardiac segmentation. 2018. ⟨hal-01793441⟩



Record views