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Simultaneous Lung Field Detection and Segmentation for Pediatric Chest Radiographs

  • Wei Zhang
  • Guanbin Li
  • Fuyu Wang
  • Longjiang E
  • Yizhou Yu
  • Liang Lin
  • Huiying LiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Accurate lung field segmentation (LFS) method is highly demanded in computer-aid diagnosis (CAD) system. However, LFS in pediatric CXR images has received few attention due to the lack of publicly available dataset and the challenges caused by their unique characteristics, such as great variations of the size, location and orientation of lungs. To fill this gap, this paper for the first time presents a simultaneous lung field detection and segmentation framework for pediatric CXR images. Our framework, called SDSLung Net, is a multi-tasking convolutional neural network architecture tailor-made for X-ray images with relatively weak appearance feature but abundant spatial rules and structural information. It is adapted from a Mask R-CNN framework [1] by incorporating a newly designed Organ Structure-Aware Encoding layer in the backbone network for more accurate spatial variation and structural representation, in parallel with a deeply supervised fully convolutional network based segmentation branch for precise lung field segmentation inside detected bounding box. Moreover, we also constructed a new and so far the largest pediatric CXR dataset with pixelwise lung field annotations. Experimental results demonstrate that our proposed SDSLung is capable of achieving significantly superior performance over state-of-the-art LFS methods on our large-scale pediatric CXR dataset and also achieving extremely competitive results on adults’ CXR dataset.

Keywords

Pediatric CXR images Lung field segmentation Segmentation Detection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Zhang
    • 1
  • Guanbin Li
    • 1
  • Fuyu Wang
    • 1
  • Longjiang E
    • 2
  • Yizhou Yu
    • 3
  • Liang Lin
    • 1
  • Huiying Liang
    • 2
    Email author
  1. 1.Sun Yat-sen UniversityGuangzhouChina
  2. 2.Institute of Pediatrics, Guangzhou Women and Children’s Medical CenterGuangzhou Medical UniversityGuangzhouChina
  3. 3.Deepwise AI LabBeijingChina

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