Liver Segmentation with Semi-Supervised Learning

DOI

10.2991/nceece-15.2016.63How to use a DOI?

Keywords

Liver Segmentation; Interactive Method; Maximal Similarity; Blocks Merging; Semi-supervised Learning

Abstract

Efficient liver segmentation from volume data provides important assistance for minimal invasive surgery and treatment. However, this task suffers from the special anatomy and topological changes. This paper presents a robust interactive method, which treats it as a semi-supervised learning task. An initial classification is performed to partition the volume data into homogeneous blocks to guide the segmentation. It is easy to implement and a more general linear or nonlinear model can be formed by virtue of semi-supervised learning. Experimental results demonstrate the performance of the proposed scheme in liver contours extracting.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Yonghui
AU  - Liu Xiaoxiao
PY  - 2015/12
DA  - 2015/12
TI  - Liver Segmentation with Semi-Supervised Learning
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 312
EP  - 319
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.63
DO  - 10.2991/nceece-15.2016.63
ID  - Yonghui2015/12
ER  -