TY - GEN
T1 - Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels)
AU - Gu, Lin
AU - Zheng, Yinqiang
AU - Bise, Ryoma
AU - Sato, Imari
AU - Imanishi, Nobuaki
AU - Aiso, Sadakazu
N1 - Funding Information:
Acknowledgments. This work was funded by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Image segmentation
KW - Random forest
KW - Semi-supervised learning
KW - Super pixels(voxels)
UR - http://www.scopus.com/inward/record.url?scp=85029384171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029384171&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66182-7_80
DO - 10.1007/978-3-319-66182-7_80
M3 - Conference contribution
AN - SCOPUS:85029384171
SN - 9783319661810
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 702
EP - 710
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Descoteaux, Maxime
A2 - Duchesne, Simon
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Maier-Hein, Lena
PB - Springer Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -