Fig. 3From: Tasks for artificial intelligence in prostate MRITypical training/inference/evaluation workflow of segmentation AI showing a three-dimensional CNN U-Net for automated segmentation of the prostatic urethra. First, the raw T2-weighted image undergoes preprocessing for intensity normalization, size scaling/cropping, and for training the image undergoes additional data augmentations. Second, the preprocessed image is fed into the CNN which outputs the prediction or white segmentation. Third, the ground truth red urethral contour is compared to the AI-predicted white contour, the loss or difference is computed, and this loss is communicated back to the CNN for tuning of neuronal weights. At the final stage, the performance evaluation of the AI model is conducted using the Dice similarity coefficient. AI Artificial intelligence, CNN Convolutional neural networkBack to article page