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

研究成果: Conference contribution

8 引用 (Scopus)

抄録

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.

元の言語English
ホスト出版物のタイトルMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
出版者Springer Verlag
ページ702-710
ページ数9
10433 LNCS
ISBN(印刷物)9783319661810
DOI
出版物ステータスPublished - 2017
イベント20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
継続期間: 2017 9 112017 9 13

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10433 LNCS
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Canada
Quebec City
期間17/9/1117/9/13

Fingerprint

Semi-supervised Learning
Supervised learning
Voxel
Image segmentation
Image Segmentation
Classifiers
Pixel
Pixels
Classifier
Random Forest
Confidence
MATLAB
Segmentation
Tend

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

これを引用

Gu, L., Zheng, Y., Bise, R., Sato, I., Imanishi, N., & Aiso, S. (2017). Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels). : Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (巻 10433 LNCS, pp. 702-710). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_80

Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels). / Gu, Lin; Zheng, Yinqiang; Bise, Ryoma; Sato, Imari; Imanishi, Nobuaki; Aiso, Sadakazu.

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. 巻 10433 LNCS Springer Verlag, 2017. p. 702-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻 10433 LNCS).

研究成果: Conference contribution

Gu, L, Zheng, Y, Bise, R, Sato, I, Imanishi, N & Aiso, S 2017, Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels). : Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. 巻. 10433 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 巻. 10433 LNCS, Springer Verlag, pp. 702-710, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/11. https://doi.org/10.1007/978-3-319-66182-7_80
Gu L, Zheng Y, Bise R, Sato I, Imanishi N, Aiso S. Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels). : Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. 巻 10433 LNCS. Springer Verlag. 2017. p. 702-710. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-66182-7_80
Gu, Lin ; Zheng, Yinqiang ; Bise, Ryoma ; Sato, Imari ; Imanishi, Nobuaki ; Aiso, Sadakazu. / Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels). Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. 巻 10433 LNCS Springer Verlag, 2017. pp. 702-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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