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.
Eosinophilic esophagitis (EoE) is an immune-mediated, clinicopathologic disease of the esophagus. EoE is histologically characterized by the accretion of eosinophils in the esophageal epithelium. The current practice involving manual identification of the small-scale histologic features of EoE relative to the size of the esophageal biopsies can be burdensome and prone to interpreter errors. The existing automatic, computer-assisted EoE identification approaches are typically designed as a train-from-scratch setting, which is prone to overfitting. In this study, we propose to use transfer deep-learning via both the ImageNet pre-trained ResNet50 as well as the more recent Big Transfer (BiT) model to achieve automated EoE feature identification on whole slide images. As opposed to existing deep-learning-based approaches that typically focus on a single pathological phenotype, our study investigates five EoE-relevant histologic features including basal zone hyperplasia, dilated intercellular spaces, eosinophils, lamina propria fibrosis, and normal lamina propria simultaneously. From the results, the model achieved a promising testing balanced accuracy of 61.9%, which is better than that of its trained-from-scratch counterparts.
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.
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