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.
KEYWORDS: Red blood cells, Image segmentation, Education and training, Data modeling, Object detection, Diagnostics, Performance modeling, Biopsy, Deep learning, Head
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.
Eosinophilic esophagitis (EoE) is a clinicopathological condition requiring frequent upper endoscopy (EGD) with several biopsies and time-consuming histopathological diagnosis. We acquired in vivo fingerprint and high wavenumber Raman spectra from the esophageal mucosa of children undergoing EGD and assessed the efficacy of this non-obtrusive real-time approach for determining EoE activity. Spectral bands related to lipids (e.g., 1078, 1301, 1440, 2855cm-1), proteins (e.g., 935, 1003, 1342, 2931cm-1), and water (3075-3650cm-1) were found to differentiate between active, inactive, and non-EoE patients. The results from this study indicate that RS is a promising method for point-of-care assessment of EoE.
Current diagnosis of pediatric eosinophilic esophagitis relies heavily on anesthesia and random biopsies during initial diagnosis and treatment monitoring. Investigation of patient’s saliva biochemical and biomolecular composition, and the potential biomarkers within, via surface enhanced Raman spectroscopy can provide key markers for distinguishing the EoE disease from non-EoE, including acid reflux disease. This optical modality coupled with paper-fluidic platform has the potential to provide a rapid diagnostic tool for non-invasive detection of EoE.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.