Open-EoE

The official implementation of Open-EoE

Abstract

Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease, characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation. Owing to the intricate microscopic representation of EoE in imaging, current methodologies which depend on manual identification are not only labor-intensive but also prone to inaccuracies. In this study, we develop an open-source toolkit, named Open-EoE, to perform end-to-end whole slide image (WSI) level eosinophil (Eos) detection using one line of command via Docker. Specifically, the toolkit supports three state-of-the-art deep learning-based object detection models. Furthermore, Open-EoE further optimizes the performance by implementing an ensemble learning strategy, and enhancing the precision and reliability of our results. The experimental results demonstrated that the Open-EoE toolkit can efficiently detect Eos on a testing set with 289 WSIs. At the widely accepted threshold of ≥ 15 Eos per high power field (HPF) for diagnosing EoE, the Open-EoE achieved an accuracy of 91%, showing decent consistency with pathologist evaluations. This suggests a promising avenue for integrating machine learning methodologies into the diagnostic process for EoE.

Installation

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

Model

The trained model can be found here Please put the folder into the floder <mmdetection/work_dirs>. If there is no folder in folder please create one.

Get Started

you can put your all WSIs in a folder.

1. Get the patch images

   python scn2patch.py <path/to/your/WSIfolder>
   

2. get the bounding box

   if you want to use faster-rcnn as the model:

   python gt_bbox.py faster
   

   if you want to use mask-rcnn as the model:

   python gt_bbox.py mask
   

   if you want to use centernet as the model:

   python gt_bbox.py center   
   

   if you want to use all of these three models:

   python gt_bbox.py all
   

3. get the ensemble result

   python gt_ensemble.py <path/to/your/WSIfolder>
   

4. get the maximum Eos count in HPF

   python gt_max.py <model_name>      model_name: faster; mask; centernet; ensemble
   

Quick start

Get our docker image

sudo docker pull 

Run Open-EoE

First you need to put a folder include your data named WSIs in the container

sudo docker run open-eoe