Bodypart Recognition Using Multi-stage Deep Learning

  • Zhennan Yan
  • Yiqiang Zhan
  • Zhigang Peng
  • Shu Liao
  • Yoshihisa Shinagawa
  • Dimitris N. Metaxas
  • Xiang Sean Zhou
Conference paper

DOI: 10.1007/978-3-319-19992-4_35

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)
Cite this paper as:
Yan Z. et al. (2015) Bodypart Recognition Using Multi-stage Deep Learning. In: Ourselin S., Alexander D., Westin CF., Cardoso M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science, vol 9123. Springer, Cham

Abstract

Automatic medical image analysis systems often start from identifying the human body part contained in the image. Specifically, given a transversal slice, it is important to know which body part it comes from, namely “slice-based bodypart recognition”. This problem has its unique characteristic - the body part of a slice is usually identified by local discriminative regions instead of global image context, e.g., a cardiac slice is differentiated from an aorta arch slice by the mediastinum region. To leverage this characteristic, we design a multi-stage deep learning framework that aims at: (1) discover the local regions that are discriminative to the bodypart recognition, and (2) learn a bodypart identifier based on these local regions. These two tasks are achieved by the two stages of our learning scheme, respectively. In the pre-train stage, a convolutional neural network (CNN) is learned in a multi-instance learning fashion to extract the most discriminative local patches from the training slices. In the boosting stage, the learned CNN is further boosted by these local patches for bodypart recognition. By exploiting the discriminative local appearances, the learned CNN becomes more accurate than global image context-based approaches. As a key hallmark, our method does not require manual annotations of the discriminative local patches. Instead, it automatically discovers them through multi-instance deep learning. We validate our method on a synthetic dataset and a large scale CT dataset (7000+ slices from wholebody CT scans). Our method achieves better performances than state-of-the-art approaches, including the standard CNN.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhennan Yan
    • 1
    • 2
  • Yiqiang Zhan
    • 1
  • Zhigang Peng
    • 1
  • Shu Liao
    • 1
  • Yoshihisa Shinagawa
    • 1
  • Dimitris N. Metaxas
    • 2
  • Xiang Sean Zhou
    • 1
  1. 1.Siemens HealthcareMalvernUSA
  2. 2.CBIM, Rutgers UniversityPiscatawayUSA

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