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Semi-supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels)

  • Lin Gu
  • Yinqiang Zheng
  • Ryoma Bise
  • Imari Sato
  • Nobuaki Imanishi
  • Sadakazu Aiso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

In this paper, we focus on semi-supervised learning for biomedical image segmentation, so as to take advantage of huge unlabelled data. We observe that there usually exist some homogeneous connected areas of low confidence in biomedical images, which tend to confuse the classifier trained with limited labelled samples. To cope with this difficulty, we propose to construct forest oriented super pixels(voxels) to augment the standard random forest classifier, in which super pixels(voxels) are built upon the forest based code. Compared to the state-of-the-art, our proposed method shows superior segmentation performance on challenging 2D/3D biomedical images. The full implementation (based on Matlab) is available at https://github.com/lingucv/ssl_superpixels.

Keywords

Image segmentation Semi-supervised learning Random forest Super pixels(voxels) 

Notes

Acknowledgments

This work was funded by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lin Gu
    • 1
  • Yinqiang Zheng
    • 1
  • Ryoma Bise
    • 2
  • Imari Sato
    • 1
  • Nobuaki Imanishi
    • 3
  • Sadakazu Aiso
    • 3
  1. 1.National Institute of InformaticsTokyoJapan
  2. 2.Kyushu UniversityFukuokaJapan
  3. 3.Keio UniversityTokyoJapan

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