Fully Automated Deep Learning-Based Renal Mass Detection on Multi-Parametric MRI

Abstract

Due to superior soft tissue contrast afforded by magnetic resonance imaging (MRI), there is great potential for multi-parametric MRI (mpMRI) for the detection and eventual classification of renal masses (RMs). In this study, we investigated fully automated deep learning methods for RMs detection using T2-Weighted (T2W) spin-echo and two contrast-enhanced T1-Weighted gradient-echo-corticomedullary (T1W-CM), nephrographic-phase (T1W-NG), T1-Weighted In-phase (T1W-IP) and opposed-phase (T1W-OP) images. The dataset contained mpMRI images of 108 kidney cancer patients with an average size of renal mass of $24~\pm ~7.8$ cm. In the first stage, kidneys were segmented using a 2D attention U-Net model, which was reported in a previous study. In the second stage, we tested five different state-of-the-art methods for RMs detections on mpMRI sequences. The model predictions were compared to manual annotations using precision, recall, specificity, and Dice Similarity Coefficient (DSC). The best-performing deep learning models were U-Net, U-Net++, and attention U-Net on the T2W, T1W-CM, and T1W-NG sequences respectively. Of the 5 mpMRI sequences, we also demonstrated that the T1W-CM is the most suitable for RMs detection. This automated detection of RMs in mpMRI sequences may be useful for the subsequent characterization of RMs in a fully automated artificial intelligence-based pipeline.

Keywords

• Multi-parametric MRI
• kidney cancer
• computer-aided detection of cancer
• deep learning