Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation

Prostate cancer is one of the leading causes of cancer deaths in men population in United States. The motivation of this study is to investigate the feasibility of developing a deep learning based computer-aided detection (CAD) scheme for prostate cancer detection from multi-parametric magnetic resonance images (mpMRIs). The proposed scheme consists of a prostate segmentation stage and a tumor detection stage. In the first stage, we adopt a state-of-art fully convolutional network (FCN) architecture with residual connections to segment prostate areas from mpMRIs including T2-weighted (T2W), T1 and diffusion-weighted imaging (DWI). We demonstrate that the proposed mpMRI based segmentation scheme yield better performance than the previous T2W based schemes. In the second stage, we present a cascaded training strategy to train a FCN with a weighted cross entropy loss function for tumor detection from mpMRIs including T2W, DWI, apparent diffusion coefficient (ADC) and K-trans. By experiments we demonstrate promising detection performance of the proposed CAD scheme.