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

  • Adam P. Harrison
  • Ziyue Xu
  • Kevin George
  • Le Lu
  • Ronald M. Summers
  • Daniel J. Mollura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

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.

Keywords

Progressive and multi-path convolutional neural networks Holistically nested neural networks Pathological lung segmentation 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adam P. Harrison
    • 1
  • Ziyue Xu
    • 1
  • Kevin George
    • 1
  • Le Lu
    • 1
  • Ronald M. Summers
    • 1
  • Daniel J. Mollura
    • 1
  1. 1.National Institutes of HealthBethesdaUSA

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