Automated tubular morphometric visualization for whole kidney biopsy

Neil Kavthekar; Brandon Ginley; Samuel Border; Nicholas Lucarelli; Kuang-Yu Jen; Pinaki Sarder

doi:10.1117/12.2613496

4 April 2022 Automated tubular morphometric visualization for whole kidney biopsy

Neil Kavthekar, Brandon Ginley, Samuel Border, Nicholas Lucarelli, Kuang-Yu Jen, Pinaki Sarder

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391G (2022) https://doi.org/10.1117/12.2613496
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

One of the strongest prognostic predictors of chronic kidney disease is interstitial fibrosis and tubular atrophy (IFTA). The ultimate goal of IFTA calculation is an estimation of the functional nephritic area. However, the clinical gold standard of estimation by pathologist is imprecise, primarily due to the overwhelming number of tubules sampled in a standard kidney biopsy. Artificial intelligence algorithms could provide significant benefit in this aspect as their high-throughput could identify and quantitatively measure thousands of tubules in mere minutes. Towards this goal, we use a custom panoptic convolutional network similar to Panoptic-DeepLab to detect tubules from 87 WSIs of biopsies from native diabetic kidneys and transplant kidneys. We measure 206 features on each tubule, including commonly understood features like tubular basement membrane thickness and tubular diameter. Finally, we have developed a tool which allows a user to select a range of tubule morphometric features to be highlighted in corresponding WSIs. The tool can also highlight tubules in WSI leveraging multiple morphometric features through selection of regions-of-interest in a uniform manifold approximation and projection plot.

Citation Download Citation

Neil Kavthekar, Brandon Ginley, Samuel Border, Nicholas Lucarelli, Kuang-Yu Jen, and Pinaki Sarder "Automated tubular morphometric visualization for whole kidney biopsy", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391G (4 April 2022); https://doi.org/10.1117/12.2613496

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