Much research is done to understand and prevent renal allograft rejection. Where acute rejection is brought back to a minimum, late rejection still occurs too frequent. Renal allografts with declined function are classified into rejection types by pathologists to improve treatment. This classification is based upon various grading criteria in stained pathological slides. Criteria depend on the different structures present in the kidney and quantification of inflammation. Since the digitization of pathological slides into whole slide images (WSIs), computer aided analyses have proven useful. Artificial neural networks have shown consistent in automated image analyses with quality comparable to trained experts. Here, we examine a convolutional neural networks as a means to aid analysis of renal allograft WSIs on two subjects: segmentation of kidney structures and segmentation of lymphocytes. A dataset of renal allograft biopsies from the Radboud University Medical Centre, Nijmegen, the Netherlands was used. Overall structure segmentation performance is good with a dice coefficient of 0.88. Segmentation of certain specific structures could be increased, but overall the segmentation maps are presumably valuable. Performance of lymphocyte segmentation proves more difficult with a dice coefficient of 0.60 and a large amount of false positives in lymphocyte instance detection. Segmentation performance of the model is currently not sufficient for the difficult task, but indicates future possibilities for lymphocyte segmentation using convolutional neural networks.
Automated structure segmentation and lymphocyte detection in kidney transplant whole slide images using a convolutional neural network
M. den Boer
Master thesis 2018.