Automatic segmentation of prostate cancer based on fusion multi-parameter MRI images

Prostate cancer is the second most common cancer in men worldwide. In the past few decades, multi-parameter MRI images have been used more and more in the detection of prostate cancer, and have important clinical significance in the invasive detection of prostate cancer. However, as the amount of data increases, radiologists need to devote more energy to recognizing images and simultaneously observing multiple sequences of magnetic resonance images, which is largely affected by the doctor's subjective judgment. Our goal is to build a new multi-parameter MRI-based prostate cancer tissue segmentation system to help doctors identify prostate cancer tissue from MRI data. We propose a convolutional neural network structure based on the U-net model to segment prostate cancer tissue. A neural network-based fusion algorithm is used to fuse the two-parameter MRI data, the fused image is sent to the convolutional neural network structure for training, and then the test set image is tested. Experimental results show that the algorithm can effectively segment prostate cancer tissue and has the potential for clinical application.