1. Train the prostate segmentation model using T2 images with codes under ./maskrcnn_model.  The public dataset I2CVB is available online (https://i2cvb.github.io/) for model training.  Save the best prostate segmentation model (i.e., weight1.h5)        
The input shape of Mask R-CNN was set to 512 × 512 pixels. Adam optimizer was applied. During the training process, the model with the highest dice coefficient score on the validation set was retained.