Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

The official implementation of Glo-In-One

Abstract

The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified (S-GGS, associated with hypertension-related injury), disappearing (D-GGS, a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent (O-GGS, nonspecific GGS increasing with aging) glomeruli.

Installation

Please refer to INSTALL.md for installation instructions of the detection phase.

Model

Pretrained model can be found here.

Data

The collected glomerulus from web imaging for self-supervised learning can be found here.

Glo-In-One - Image Demo

For glomerulus detection, run

python run_detection.py circledet --load_model ../model/detection_model.pth --filter_boarder --demo ../demo.svs --demo_dir ../output

For lesion characterization, run

python generate_patches.py ../demo.svs ../output
python filter_patches_5c.py --case ../demo.svs --output_dir ../output --checkpoint ../model/classifier_model.pth.tar
python filter_xml_5c.py  ../demo.svs ../output

For generating segment mask, run

python segmentation.py --rootdir ../output --wsi ../demo.svs --model ../model/segmentation_model.pth

Quick start

Get our docker image

sudo docker pull 

Run Glo-In-One

You can run the following command or change the input_dir, then you will have the final segmentation results in output_dir

# you need to specify the input directory
export input_dir=/home/input_dir   
# make that directory
sudo mkdir $input_dir
#run the docker
sudo nvidia-docker run -it --rm -v $input_dir:/INPUTS/ -v $output_dir:/OUTPUTS