MO055: Multi-Class Segmentation of Kidney Tissues using Convolutional Neuronal Networks (CNNS)

Abstract

Abstract BACKGROUND AND AIMS Routine pathological diagnostics in kidneys are mainly based on semi-quantitative eyeballing. In own former studies, we showed predictive value of precise immune cell quantification in allografts using digital semi-automated techniques. We now aim to achieve fully automated segmentation workflow with CNNs. METHOD Standard routine stains (immuno/histochemistry, immunofluorescence) were digitized (20×) with Metafer, a commercial scanning/imaging platform. Diagnostically relevant anatomical compartments (cortex, medulla, glomeruli, tubuli [proximal/distal/collecting duct], glomerular/peritubular capillaries and nuclei) were manually annotated by use of immunomarkers to generate large data sets on human renal biopsies and nephrectomies. Data were used to train multi-class semantic segmentation CNNs with broad data augmentation to achieve a robustness against staining variances. RESULTS Using Jones-HE stains for multi-class segmentation, a cortex-medulla-extrarenal CNN revealed pixel based hit rates above 97.9%, detection of glomeruli had a pixel based hit rate above 99%, a multi-class CNN for tubules, tubular membranes and peritubular capillaries resulted in a hit rate of 91.5%, and nuclear-based cell detection shows pixel based hit rates above 98%. Identification of cell location in interstitium, tubuli, glomeruli, peritubular and glomerular capillaries reached very high hit rates: Glomerular endothelial cells actually result in 83% true positives, 13% false negatives and 4% false positives. Additionally, a tubulus classifier (proximal tubulus, distal tubulus, collecting duct and atrophic tubulus) with an accuracy >90% was developed. CONCLUSION Automated structure segmentation by CNNs can complement and specify classical nephropathological diagnostics, especially for spatial risk marker evaluation in early transplant biopsies.