Kavthekar Neil, Ginley Brandon, Border Samuel, Lucarelli Nicholas, Jen Kuang-Yu, Sarder Pinaki
Departments of Biomedical Engineering, University at Buffalo, the State University of New York.
Pathology & Anatomical Sciences, University at Buffalo, the State University of New York.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12039. doi: 10.1117/12.2613496. Epub 2022 Apr 4.
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.
慢性肾脏病最强的预后预测指标之一是间质纤维化和肾小管萎缩（IFTA）。计算IFTA的最终目标是估计功能性肾实质面积。然而，病理学家进行估计的临床金标准并不精确，主要原因是在标准肾活检中采样的肾小管数量过多。人工智能算法在这方面可以提供显著的益处，因为它们的高通量能够在短短几分钟内识别并定量测量数千个肾小管。为了实现这一目标，我们使用了一个类似于全景深度实验室（Panoptic-DeepLab）的定制全景卷积网络，从87张来自原发性糖尿病肾病和移植肾活检的全切片图像（WSIs）中检测肾小管。我们在每个肾小管上测量206个特征，包括诸如肾小管基底膜厚度和肾小管直径等常见特征。最后，我们开发了一种工具，允许用户选择一系列肾小管形态计量特征，以便在相应的全切片图像中突出显示。该工具还可以通过在统一流形近似和投影图中选择感兴趣区域，利用多个形态计量特征突出显示全切片图像中的肾小管。
