SVF-Net: Learning Deformable Image Registration Using Shape Matching View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Marc-Michel Rohé , Manasi Datar , Tobias Heimann , Maxime Sermesant , Xavier Pennec

ABSTRACT

In this paper, we propose an innovative approach for registration based on the deterministic prediction of the parameters from both images instead of the optimization of a energy criteria. The method relies on a fully convolutional network whose architecture consists of contracting layers to detect relevant features and a symmetric expanding path that matches them together and outputs the transformation parametrization. Whereas convolutional networks have seen a widespread expansion and have been already applied to many medical imaging problems such as segmentation and classification, its application to registration has so far faced the challenge of defining ground truth data on which to train the algorithm. Here, we present a novel training strategy to build reference deformations which relies on the registration of segmented regions of interest. We apply this methodology to the problem of inter-patient heart registration and show an important improvement over a state of the art optimization based algorithm. Not only our method is more accurate but it is also faster - registration of two 3D-images taking less than 30 ms second on a GPU - and more robust to outliers. More... »

PAGES

266-274

References to SciGraph publications

  • 2012. Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration in STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES
  • 2005-02. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2016. Fast Predictive Image Registration in DEEP LEARNING AND DATA LABELING FOR MEDICAL APPLICATIONS
  • 2013. Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach in DEFORMATION MODELS
  • 2006. A Log-Euclidean Framework for Statistics on Diffeomorphisms in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2006
  • 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Book

    TITLE

    Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

    ISBN

    978-3-319-66181-0
    978-3-319-66182-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-66182-7_31

    DOI

    http://dx.doi.org/10.1007/978-3-319-66182-7_31

    DIMENSIONS

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