Deep learning-based open source toolkit for eosinophil detection in pediatric eosinophilic esophagitis

Juming Xiong; Yilin Liu; Ruining Deng; Regina N. Tyree; Hernan Correa; Girish Hiremath; Yaohong Wang; Yuankai Huo

doi:10.1117/12.3006520

3 April 2024 Deep learning-based open source toolkit for eosinophil detection in pediatric eosinophilic esophagitis

Juming Xiong, Yilin Liu, Ruining Deng, Regina N. Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, Yuankai Huo

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330X (2024) https://doi.org/10.1117/12.3006520
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

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, OpenEoE 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. The docker and source code has been made publicly available at https://github.com/hrlblab/Open-EoE.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Juming Xiong, Yilin Liu, Ruining Deng, Regina N. Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, and Yuankai Huo "Deep learning-based open source toolkit for eosinophil detection in pediatric eosinophilic esophagitis", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330X (3 April 2024); https://doi.org/10.1117/12.3006520

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