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Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

  • Sangheum Hwang
  • Sunggyun Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

We introduce an accurate lung segmentation model for chest radiographs based on deep convolutional neural networks. Our model is based on atrous convolutional layers to increase the field-of-view of filters efficiently. To improve segmentation performances further, we also propose a multi-stage training strategy, network-wise training, which the current stage network is fed with both input images and the outputs from pre-stage network. It is shown that this strategy has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.

Keywords

Lung segmentation Network-wise trainnig Atrous convolution 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Lunit Inc.SeoulKorea

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