Unsupervised Image-to-Image Translation in multi-parametric MRI of Bladder Cancer

Detection of muscular invasive bladder cancer (MIBC) is critical for surgical selection of bladder cancer (BCa) patients. Currently, multi-parameter magnetic resonance imaging (mp-MRI) is the predominant approach for identifying MIBC. However, mp-MRI is still insufficient due to the presence of noise and artifacts. Our research aims to synthesize images from the existing sequences of mp-MRI to substitute missing or low signal-to-noise ratio sequences through image-to-image (I2I) translation. Using mp-MRI images of 255 BCa patients collected in our department, we here propose a one-to-many unsupervised I2I translation network with region-wise semantic enhancement to synthesize virtual samples. We introduce an improved adaptive instance normalization module to support the generator for synthesizing multi-domain images. In addition, a branch for region-wise semantic segmentation helps the generator to enhance the quality of image translation for a specific region. A semantically consistent loss is applied to maintaining the consistency between the synthesized and the input images via region-wise semantic segmentation. Experiments on the BraTS and BCa datasets indicate that our I2I translation approach outperforms several state of the art methods. Additionally, we perform clinical feasibility tests using the synthesis images. The clinicians reach a consensus between the Vesical Imaging Reporting and Data System (VI-RADS) scoring results from the synthesized and the real mp-MRI images. In addition, after the BCa training set has been expanded using the proposed generation model, the accuracy of the BCa muscular invasion classification is improved from 77.78% to 85.19%.