CT Prostate Segmentation Based on Continuously Updated Random Forests
- https://doi.org/10.2991/icmmita-15.2015.75How to use a DOI?
- Prostate Segmentation; Treatment Images; Continuously Updated Random Forests
It is important to segment prostate automatically in the daily treatment images. However, previous methods often ignore the previous segmented treatment images which contain valuable patient-specific information. To this end, this paper proposes a novel CT prostate segmentation method based on a random forest model which is trained as a classifier to segment prostates. This model can be continuously updated by adding newly segmented prostate shapes into the training pool. In this way, more patient-specific information is incorporated into the training procedure. The experimental results show that the proposed method can improve the accuracy of prostate segmentation efficiently.
- © 2015, 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/).
Cite this article
TY - CONF AU - Huangjian Deng AU - Xiubin Dai AU - Dandan Shi PY - 2015/11 DA - 2015/11 TI - CT Prostate Segmentation Based on Continuously Updated Random Forests BT - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 384 EP - 389 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-15.2015.75 DO - https://doi.org/10.2991/icmmita-15.2015.75 ID - Deng2015/11 ER -