Automatic Segmentation of Prostate Structures Using a Convolutional Neural Network from Multiparametric MRI

Prostate cancer is the second highest incidence of cancer in men worldwide. Therefore, early detection of prostate cancer is of great clinical significance. In the past few decades, multi-parametric MRI has been increasingly used in the detection of prostate cancer. However, this requires the radiologists to have a higher degree of prostate MRI reading experiences, which may be greatly influenced by the subjective judgment. Our goal is to establish a prostate cancer tissue segmentation system based on multiparametric MRI to help doctors automatically define prostate cancer regions. We proposed a convolutional neural network architecture based on the U-net model for segmentation of prostate cancer tissue. The multiparametric MRI images of the pelvis are rescaled, merged together, and delivered into a convolutional neural network architecture for training. Test images of the pelvis were used to evaluate the capability of the trained CNN architecture. The results showed that the proposed algorithm can efficiently segment prostate cancer tissue with a high accuracy and can be applied to computer assisted prostate cancer diagnosis.