Regressing Heatmaps for Multiple Landmark Localization Using CNNs View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Christian Payer , Darko Štern , Horst Bischof , Martin Urschler

ABSTRACT

We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data. More... »

PAGES

230-238

References to SciGraph publications

  • 2014. A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2014
  • 2014. Towards Automatic Bone Age Estimation from MRI: Localization of 3D Anatomical Landmarks in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2014
  • 2015. 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2015
  • 2013. Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 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 2016

    ISBN

    978-3-319-46722-1
    978-3-319-46723-8

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-46723-8_27

    DOI

    http://dx.doi.org/10.1007/978-3-319-46723-8_27

    DIMENSIONS

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