Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Adam P. Harrison , Ziyue Xu , Kevin George , Le Lu , Ronald M. Summers , Daniel J. Mollura

ABSTRACT

Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS is often a pre-requisite for other imaging analytics, methodological simplicity and generality are key factors in usability. Along those lines, we present a bottom-up deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape. We incorporate the deeply supervised learning framework, but enhance it with a simple, yet effective, progressive multi-path scheme, which more reliably merges outputs from different network stages. The result is a deep model able to produce finer detailed masks, which we call progressive holistically-nested networks (P-HNNs). Using extensive cross-validation, our method is tested on a multi-institutional dataset comprising 929 CT scans (848 publicly available), of pathological lungs, reporting mean dice scores of 0.985 and demonstrating significant qualitative and quantitative improvements over state-of-the art approaches. More... »

PAGES

621-629

References to SciGraph publications

  • 2016. Dense Volume-to-Volume Vascular Boundary Detection in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -- MICCAI 2016
  • 2016. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2011. Multi-stage Learning for Robust Lung Segmentation in Challenging CT Volumes in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2016. Automatic Lymph Node Cluster Segmentation Using Holistically-Nested Neural Networks and Structured Optimization in CT Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2014. Segmentation of Lungs with Interstitial Lung Disease in CT Scans: A TV-L1 Based Texture Analysis Approach in ADVANCES IN VISUAL COMPUTING
  • 2016. Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 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

    978-3-319-66178-0
    978-3-319-66179-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-66179-7_71

    DOI

    http://dx.doi.org/10.1007/978-3-319-66179-7_71

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1091432029


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