Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation
Yunzhi Wang, Bin Zheng, Dashan Gao, Jiao Wang · 2018
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.