3 research outputs found
Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis
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. The docker
and source code has been made publicly available at
https://github.com/hrlblab/Open-EoE
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation
Eosinophilic esophagitis (EoE) is a chronic and relapsing disease
characterized by esophageal inflammation. Symptoms of EoE include difficulty
swallowing, food impaction, and chest pain which significantly impact the
quality of life, resulting in nutritional impairments, social limitations, and
psychological distress. The diagnosis of EoE is typically performed with a
threshold (15 to 20) of eosinophils (Eos) per high-power field (HPF). Since the
current counting process of Eos is a resource-intensive process for human
pathologists, automatic methods are desired. Circle representation has been
shown as a more precise, yet less complicated, representation for automatic
instance cell segmentation such as CircleSnake approach. However, the
CircleSnake was designed as a single-label model, which is not able to deal
with multi-label scenarios. In this paper, we propose the multi-label
CircleSnake model for instance segmentation on Eos. It extends the original
CircleSnake model from a single-label design to a multi-label model, allowing
segmentation of multiple object types. Experimental results illustrate the
CircleSnake model's superiority over the traditional Mask R-CNN model and
DeepSnake model in terms of average precision (AP) in identifying and
segmenting eosinophils, thereby enabling enhanced characterization of EoE. This
automated approach holds promise for streamlining the assessment process and
improving diagnostic accuracy in EoE analysis. The source code has been made
publicly available at https://github.com/yilinliu610730/EoE