AbstractAllograft rejection is a major concern in kidney transplantation. Inflammatory processes in patients with kidney allografts involve various patterns of immune cell recruitment and distributions. Lymphoid aggregates (LAs) are commonly observed in patients with kidney allografts and their presence and localization may correlate with severity of acute rejection. Alongside with other markers of inflammation, LAs assessment is currently performed by pathologists manually in a qualitative way, which is both time consuming and far from precise. Here we present the first automated method of identifying LAs and measuring their densities in whole slide images of transplant kidney biopsies. We trained a deep convolutional neural network based on U-Net on 44 core needle kidney biopsy slides, monitoring loss on a validation set (n=7 slides). The model was subsequently tested on a hold-out set (n=10 slides). We found that the coarse pattern of LAs localization agrees between the annotations and predictions, which is reflected by high correlation between the annotated and predicted fraction of LAs area per slide (Pearson R of 0.9756). Furthermore, the network achieves an auROC of 97.78 ± 0.93% and an IoU score of 69.72 ± 6.24 % per LA-containing slide in the test set. Our study demonstrates that a deep convolutional neural network can accurately identify lymphoid aggregates in digitized histological slides of kidney. This study presents a first automatic DL-based approach for quantifying inflammation marks in allograft kidney, which can greatly improve precision and speed of assessment of allograft kidney biopsies when implemented as a part of computer-aided diagnosis system.
Automated Localization and Segmentation of Mononuclear Cell Aggregates in Kidney Histological Images Using Deep Learning
