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Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

  • Maayan Frid-AdarEmail author
  • Avi Ben-Cohen
  • Rula Amer
  • Hayit Greenspan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.

Keywords

Chest radiographs Lung segmentation Clavicle segmentation Heart segmentation Fully convolutional networks 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Maayan Frid-Adar
    • 1
    Email author
  • Avi Ben-Cohen
    • 2
  • Rula Amer
    • 2
  • Hayit Greenspan
    • 1
    • 2
  1. 1.RADLogics Ltd.Tel AvivIsrael
  2. 2.Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing LaboratoryTel Aviv UniversityTel AvivIsrael

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