We propose a learning-based method to automatically segment prostate and its dominant intraprostatic lesions (DILs) from positron emission tomography (PET)/computed tomography (CT) images. A dual attention mask R-CNN is introduced to enable end-to-end segmentation. To avoid the effect of useless region, mask R-CNN is used to get rid of non-tumor regions via fist locate the tumor region-of-interest (ROI) and then segment tumor within that ROI. Dual attention networks are used as backbone in mask R-CNN to extract comprehensive features from both CT and PET images. The binary mask of tumor of an arrival patient’s CT and PET image is generated by the well-trained network. To evaluate the proposed method, we retrospectively investigate 25 PET/CT datasets. On each dataset, prostate and DILs were delineated by physicians and was served as ground truth and training target. The proposed method was trained and evaluated by a Five-fold cross validation strategy. The average centroid distance, volume difference and DSC value for prostate/DIL among all 25 patients are 0.83±0.91mm, -0.01±0.79 and 0.84±0.09, respectively. The proposed method has great potential in improving the efficiency and mitigating the observer-dependence in prostate and DIL contouring for DIL focal boost radiation therapy
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