Abstract

Accurate prostate segmentation in CT images is a signififi- cant yet challenging task for image guided radiotherapy. In this paper, a novel semi-automated prostate segmentation method is presented. Specififically, to segment the prostate in the current treatment image, the physician fifirst takes a few seconds to manually specify the fifirst and last slices of the prostate in the image space. Then, the prostate is segmented automatically by the proposed two steps: (i) The fifirst 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 fifinal 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 specifification of the fifirst and last slices of the prostate