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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.

CT Prostate Segmentation Based on Continuously Updated Random Forests