Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases

Abstract

Podocyte injury plays a key role in pathogenesis of many kidney diseases with increased podocyte foot process width (FPW), an important measure of podocyte injury. Unfortunately, there is no consensus on the best way to estimate FPW and unbiased stereology, the current gold standard, is time consuming and not widely available. To address this, we developed an automated FPW estimation technique using deep learning. A U-Net architecture variant model was trained to semantically segment the podocyte-glomerular basement membrane interface and filtration slits. Additionally, we employed a post-processing computer vision approach to accurately estimate FPW. A custom segmentation utility was also created to manually classify these structures on digital electron microscopy (EM) images and to prepare a training dataset. The model was applied to EM images of kidney biopsies from 56 patients with Fabry disease, 15 with type 2 diabetes, 10 with minimal change disease, and 17 normal individuals. The results were compared with unbiased stereology measurements performed by expert technicians unaware of the clinical information. FPW measured by deep learning and by the expert technicians were highly correlated and not statistically different in any of the studied groups. A Bland-Altman plot confirmed interchangeability of the methods. FPW measurement time per biopsy was substantially reduced by deep learning. Thus, we have developed a novel validated deep learning model for FPW measurement on EM images. The model is accessible through a cloud-based application making calculation of this important biomarker more widely accessible for research and clinical applications.

Keywords: Fabry; deep learning; foot process; foot process width; machine learning; podocyte.

Figure 1.. Model of the ForkNet Architecture.

Top: A schematic representation of the architecture showing an input EM Image to the left, conv2d blocks max pooling for downscaling, split branches, deconv2d and conv2d blocks for upscaling, and output layers to the right. Layer connections are identified as solid directional lines, and skip connections are identified as dashed directional lines. Note: the skip connection from the membrane branch to the slit branch. Most residual connections used convolutional blocks rather than identity connections. Bottom: A-E and F-J show two examples of input images (A and F), resulting in corresponding PGBMI (B and G) and slit masks (C and H) with merged images, showing the masks superimposed on the input images (D and I); and the post-processing output with individual foot process width (FPW) measurements (E and J). F’-J’ show magnified views of the red boxes in corresponding F-J images. The size of the dots in the slit mask and merged images reflects the model confidence in prediction of a slit. The scale bar in A, F, and F’ represents 1 μm.

Figure 2.. ForkNet post-processing script general workflow.

Top left image: a low magnification view of a glomerulus. The small squares represent systematic uniform random sampling (SURS) images taken at higher magnification (30,000 x) shown in bottom left that are used for foot process width (FPW) measurements. The flowchart on the right shows the post-processing workflow. The input images are normalized and go through various filters and processing, the output of which will be semantic segmentation of filtration slits and podocyte – glomerular basement membrane interface (PGBMI). The length of the individual segments (PGBMI limited between two adjacent slits) is measured and exported. Different components of the architecture are shown in boxes coded with different shapes and shades that are connected through solid or dashed lines as explained in the “Key” box at the bottom left corner. In summary, from left to right, the input image goes through conv2d downscaling blocks, split branches, and deconv2d upscaling to result in PGBMI and slit output masks. ROI: region of interest. The scale bar in the bottom high mag image represents 1 μm.

Figure 3.. Interchangeability of deep learning (DL) and expert technician (ET) measurements of foot process width (FPW).

(A) Comparison of DL (circles) and ET (triangles) FPW measurements in control subjects (grey), males with Fabry disease (red), females with Fabry disease (green), diabetic kidney disease (DKD; blue), and minimal change disease (MCD; pink). The corresponding violin plots (grey) are shown in the background of individual values. Dashed lines in the violin plots show means and dotted lines show SD. P-values of DL vs. ET measurement comparisons are shown above the violin plots (ns = non-significant; asterisk = both DL or ET values are statistically significantly different from controls); (B) Simple linear correlation between DL and ET FPW measurements in the entire cohort. The regression line (dashed red) and the line of identity (solid black) are almost superimposed (R=0.92; p<0.0001). The black dotted lines show the 95% confidence limit of the regression line; (C) Bland-Altman graph plotting the averages of FPW measurements by DL and ET vs. the differences between the values measured by the two methods in the entire cohort. The middle dotted line shows the bias of DL measurements (in nm) and the two other dotted lines show the upper and lower limits of agreement defined by ± 1.96 SD. A color code key is provided on the right; (D-H) Representative electron micrographs (~30,000x) from biopsies from the various groups shown in the above graphs.

Figure 4.. (A-E) Comparison of individual foot process width distributions in Fabry disease, DKD, MCD, and control subjects.

Histogram of aggregate individual foot process widths (FPWs) in biopsies from (A) control subjects, (B) female patients with Fabry disease, (C) male patients with Fabry disease, (D) subjects with diabetic kidney disease (DKD), and (E) patients with minimal change disease (MCD). The gray histogram of individual FPW distribution of controls is added to the background of each of the other categories for comparison. All FPW ≥ 2000 nm are aggregated in the last bucket, with the number above the last bar representing the %FPW measurements ≥ 2000 nm. (F) Intact foot processes in a control biopsy. (G) Segmental foot process widening (red arrowheads) in a biopsy from a patient with Fabry disease. The blue lines in F and G show podocyte glomerular basement membrane interface and the yellow dots show the slit diaphragms segmented by DL. The scale bars represent 1 μm. (H) Interglomerular variability average FPW among the groups studies (gray: control; green: Fabry female; red: Fabry male; blue: DND; and purple: MCD). Each circle represents average FPW in a glomerulus. Each vertical line represents a biopsy. Small black dots represent medians. (I) comparison of coefficient of variation (CV) of interglomerular average FPW per group.

Figure 5.. Convergence of average FPW by incremental sampling of images in biopsies from (A) controls, (B) females with Fabry disease, (C) males with Fabry disease, (D) DKD, and (E) MCD.

X-axis: number of input EM images used to obtain average FPW; left Y-axis: biopsy FPW pixel average represented in the colored graphs (shades of green to purple); right Y-axis: biopsy convergence cumulative distribution function (CDF) represented in the stepped line (grey). The vertical blue dashed line shows the number of images resulting in a CDF = 0.85.