Wireless Personal Communications

, Volume 101, Issue 1, pp 511–529 | Cite as

LF-SegNet: A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs

  • Ajay Mittal
  • Rahul Hooda
  • Sanjeev Sofat


Segmentation of lung fields is an important pre-requisite step in chest radiographic computer-aided diagnosis systems as it precisely defines the region-of-interest on which different operations are applied. However, it is immensely challenging due to extreme variations in shape and size of lungs. Manual segmentation is also prone to large inter-observer and intra-observer variations. Thus, an automated method for lung field segmentation with sufficiently high accuracy is unsparingly required. This paper presents a deep learning-based fully convolutional encoder-decoder network for segmenting lung fields from chest radiographs. The major contribution of this work is in the unique design of the encoder-decoder network that makes it especially suitable for lung field segmentation. The proposed network is trained, tested and evaluated on publicly available standard datasets. The result of evaluation indicates that the performance of the proposed method, i.e. accuracy of 98.73% and overlap of 95.10%, is better than state-of-the-art methods.


Medical image analysis Deep-learning Lung field segmentation Chest radiographs Encoder-decoder network 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University Institute of Engineering and Technology, Panjab UniversityChandigarhIndia
  2. 2.Department of Computer Science and EngineeringPunjab Engineering College (Deemed to be University)ChandigarhIndia

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