Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso

Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2227-2234

Abstract


Accurate prostate segmentation in CT images is a significant yet challenging task for image guided radiotherapy. In this paper, a novel semi-automated prostate segmentation method is presented. Specifically, to segment the prostate in the current treatment image, the physician first takes a few seconds to manually specify the first and last slices of the prostate in the image space. Then, the prostate is segmented automatically by the proposed two steps: (i) The first step of prostate-likelihood estimation to predict the prostate likelihood for each voxel in the current treatment image, aiming to generate the 3-D prostate-likelihood map by the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) The second step of multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from the planning and previous treatment images. The experimental result shows that the proposed method outperforms several state-of-the-art methods on prostate segmentation in a real prostate CT dataset, consisting of 24 patients with 330 images. Moreover, it is also clinically feasible since our method just requires the physician to spend a few seconds on manual specification of the first and last slices of the prostate.

Related Material


[pdf]
[bibtex]
@InProceedings{Shi_2013_CVPR,
author = {Shi, Yinghuan and Liao, Shu and Gao, Yaozong and Zhang, Daoqiang and Gao, Yang and Shen, Dinggang},
title = {Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}