MRI-based prostate and dominant lesion segmentation using deep neural network

In this study, a learning-based method using mask R-CNN is proposed to automatically segment prostate and its dominant intraprostatic lesions (DILs) from magnetic resonance (MR) images. The mask RCNN is able to perform end-to-end segmentation by locating the target region-of-interest (ROI) and then segmenting target within that ROI. The ROI locating step can improve the efficiency of the segmentation step by decreasing the image size. Dual attention networks are used as backbone in mask R-CNN to extract comprehensive features from MR images. The binary mask of targets of an arrival patient's MR image is generated by the well-trained network. To evaluate the proposed method, we retrospectively investigate 25 MRI 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 0.85±2.62mm/2.77±2.13, 0.58±0.52cc/1.72±1.74cc and 0.95±0.09/0.69±0.12, respectively. The proposed method has shown accurate segmentation performance, which is promising in improving the efficiency and mitigating the observer-dependence in prostate and DIL contouring for DIL focal boost radiation therapy.