Structural information and (hyper)graph matching for MRI piglet brain extraction

  title={Structural information and (hyper)graph matching for MRI piglet brain extraction},
  author={Alain Durandeau and Jean-Baptiste Fasquel and Isabelle Bloch and Edouard Mazerand and Philippe Menei and Claudia N. Montero-Menei and Mickael Dinomais},
  journal={10th International Conference on Pattern Recognition Systems (ICPRS-2019)},
In the context of the study of the maturation process of the infant brain, this paper focuses on postnatal piglet brain, whose structure is similar to the one of an infant. Due to the small size of the piglet brain and the abundance of surrounding fat and muscles, the automatic brain extraction using correctely initialized deformable models is tedious, and the standard approach used for human brain does not apply. The paper proposes an original brain extraction method based on a deformable… 

Figures and Tables from this paper

Automated identification of piglet brain tissue from MRI images using Region-Based Convolutional Neural Networks
The use of Mask R-CNN, a Region-based Convolutional Neural Network (R-CNN), for automated brain extractions of piglet brains is demonstrated to provide a viable tool for skull stripping of piglets' brains.


A review on automatic fetal and neonatal brain MRI segmentation
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
A Graph Based Image Interpretation Method Using A Priori Qualitative Inclusion and Photometric Relationships
Results show the potential of the method to recover, in a reasonable runtime, expected regions, a priori described in a qualitative manner, from an initial oversegmentation using qualitative knowledge of its content.
A novel hypergraph matching algorithm based on tensor refining
Fuzzy sets for image processing and understanding
  • I. Bloch
  • Computer Science
    Fuzzy Sets Syst.
  • 2015
Approach for sequential image interpretation using a priori binary perceptual topological and photometric knowledge and k-means-based segmentation.
The main contribution concerns the parameterization of the k-means clustering algorithm, to be used during the segmentation procedure, and the graph-matching-based identification of resulting clusters, corresponding to regions declared in graphs.
A tensor-based algorithm for high-order graph matching
The proposed approach to establishing correspondences between two sets of visual features using higher-order constraints instead of the unary or pairwise ones used in classical methods is compared to state-of-the-art algorithms on both synthetic and real data.