Dense Volume-to-Volume Vascular Boundary Detection View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Jameson Merkow , Alison Marsden , David Kriegman , Zhuowen Tu

ABSTRACT

In this work, we tackle the important problem of dense 3D volume labeling in medical imaging. We start by introducing HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED). Next, we develop a novel 3D-Convolutional Neural Network (CNN) architecture, I2I-3D, that predicts boundary location in volumetric data. Our fine-to-fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label training and prediction (2) precise voxel-level prediction to capture fine scale structures prevalent in medical data and (3) directed multi-scale, multi-level feature learning. We evaluate our approaches on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. We show that our deep learning approaches out-perform the current state-of-the-art in 3D vascular boundary detection (structured forests 3D), by a large margin, as well as HED applied to slices. Prediction takes about one minute on a typical \(512\,\times \,512\,\times \,512\) volume, when using GPU. More... »

PAGES

371-379

References to SciGraph publications

  • 2015. Structural Edge Detection for Cardiovascular Modeling in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • 2015. 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2015
  • 2015. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2015
  • 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

    978-3-319-46725-2
    978-3-319-46726-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-46726-9_43

    DOI

    http://dx.doi.org/10.1007/978-3-319-46726-9_43

    DIMENSIONS

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