PodoCount: A tool for whole-slide podocyte quantification (v1)

Version 1.0.0

This repository contains the source codes for the publication, "PodoCount: A robust, fully atuomated whole-slide podocyte quantification tool." All algorithms were developed and written by Briana Santo. The function xmltomask has been adapted from Lutnick et al.'s work "An integrated iterative annotation technique for easing neural network training in medical image analysis," Nature Machine Intelligence, 2019.

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A pre-print version of this work is available at: X

Prepared by Briana Santo at SUNY Buffalo on 27July2021

Image Data

Whole slide images (WSIs) of murine kidney data are available at: http://bit.ly/3rdGPEd. Murine data includes whole kidney sections derived from both wild type and diseased mice across six mouse models [T2DM A, T2DM B, Aging, FSGS (SAND), HIVAN, Progeroid]. All kidney specimens were stained with p57kip2 immunohistochemistry and Periodic Acid-Schiff (without Hematoxylin counter stain) prior to digitization.

Requirements

This code runs using python3.

Dependencies

• argparse [1.1]
• cv2 [4.1.2]
• lxml.etree [4.5.0]
• matplotlib [3.3.4]
• numpy [1.18.1]
• openslide-python [1.1.1]
• pandas [0.25.3]
• scikit-image [0.17.2]
• scipy [1.5.4]

Modules from the Python Standard Library

• glob
• os
• sys
• time
• warnings

Usage:

Running PodoCount from your own computer

The pipeline is run using: podocount_main_serv.py

Download the codes folder from GitHub titled either "PodoCount_Mouse_Analysis" or "PodoCount_Human_Analysis." Within the codes folder are two distinct subfolders entitled "WSIs" and "glom_xmls". Place WSIs for pipeline analysis in the "WSIs" folder; acceptable WSI formats include .svs and .ndpi. Place glomerulus annotation files in the "glom_xmls" folder. Glomerulus annotations (.xml files) may be generated through manual annotation or via our lab's H-AI-L tool; a convolutional neural network for glomerulus boundary detection developed by Lutnick et al.

The code is run by using: podocount_main_serv.py

to run this code you must be in the "PodoCount_Mouse_Analysis" or "PodoCount_Human_Analysis" directory where it is contained, with WSIs and XMLs provided in the corresponding subfolders.

Run the main script "podocount_main_serv.py", providing the necessary flags below:

• [--ftype] flag set to the WSI file extension
• [--slider] flag set to a value [0,3]
• [--cohort] set to the dataset or experiment name
• [--section_thickness] set to the tissue section thickness (an integer value within the range [1,15])
• [--num_sections] flag set to the number of tissue sections per slide (for WSIs of murine whole kidney sections options are 1 or 2; for human biopsy data, set to 1).

For questions or feedback, please contact:

• Briana Santo basanto@buffalo.edu
• Pinaki Sarder pinakisa@buffalo.edu