Deep-learning based histopathologic assessment of kidney tissue

M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, J. Roelofs, M. Stegall, M. Alexander, B. Smith, B. Smeets, L. Hilbrands and J. van der Laak

Journal of the American Society of Nephrology (2019)



kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network’s glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. Results: The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was “glomeruli�? in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by “tubuli combined�? and “interstitium.�? The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. Conclusions: This study presents the first convolutional neural network formulticlass segmentation of PASstained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.

Request PDF

A pdf file of this publication is available for personal use. Enter your e-mail address in the box below and press the button. You will receive an e-mail message with a link to the pdf file.