Fully automated cascaded approach for renal mass detection on T2 weighted MRI images

Abstract

Due to the superior soft tissue contrast in magnetic resonance imaging (MRI), MRI may be well suited for renal mass characterization (e.g., benign vs. malignant). Though renal mass detection and characterization using deeplearning (DL) methods have been extensively studied for CT images, those same tasks are yet to be investigated on MR images. Existing algorithms for renal mass characterization require manual segmentation, therefore development of algorithms to localize and detect renal masses is important fully automatically. In this study, we developed a DL-based fully automated renal mass detection model on T2- weighted (T2W) images. In a cascaded approach, we initially segmented kidneys as a region-of-interest (ROI) using 2D U-Net model, then renal masses were detected on segmented kidneys using 2D U-Net convolutional neural network (CNN) model. We trained our model on randomly selected 80% of dataset using 5-fold cross-validation technique and evaluated on remaining 20% test cases for renal mass detection. Our T2W MRI dataset contained 108 patients with malignant (renal cell carcinoma- clear cell, papillary and chromophobe) and benign (fat poor angiomyolipoma-fpAML, oncocytomas) renal masses. The U-Net model for renal mass detection generated Dice similarity coefficient (DSC) of 90.00 ± 6.00 % (mean ± standard deviation). When localized kidneys evaluated on U-Net renal mass detection model yielded a sensitivity/recall, and specificity of 76.49% and 86.55%, respectively. Thus, our proposed fully automated cascaded approach has potential to be used as the first step in renal mass characterization study on T2W MRI images.