Glomerular Basement Membrane Thickness Estimation and Stratification via Active Semi-Supervised Learning Model

The measure of glomerular basement membrane (GBM) thickness is used as diagnostic criteria for kidney glomerular diseases. The GBM thickness measurement, a time-consuming task, is performed by expert pathologists on transmission electron microscopy (TEM) images; therefore, it is affected by subjectivity and reproducibility issues.

Here we introduce a fully automated pipeline for the GBM segmentation and successive thickness estimation, starting from TEM images. This method is based on an active semi-supervised learning training procedure of a convolutional neural network model. Starting from the areas automatically identified by the model, we provide a robust measurement of membrane thickness using pixels distance matrix and computer vision techniques. Using these values, we trained a machine learning model to automatically determine the GBM thickness. To verify the accuracy of the method, we compared the predicted results with the full iconographic materials and diagnostic record reports from 42 renal biopsies having normal thickness (n = 21), thin (n = 10), thick GBM (n = 11).

The obtained segmentations were used for the automated estimation of GBM thickness via computer vision algorithms and compared with manual measurements, obtaining a correlation of Pearson's R2 of 0.85. The GBM thickness was stratified into 3 classes, namely, normal, thin, thick, with a 0.63 Matthews correlation coefficient and a 0.76 accuracy.

The proposed pipeline obtained state-of-the-art performance in GBM segmentation, proving its robustness under image variations, such as magnification, contrast, and complex geometrical shapes. Automated measures could assist clinicians in standard clinical practice speeding up routine procedures with high diagnostic accuracy.