Abstract
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.
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