Authors:
William Adorno III
1
;
Alexis Catalano
2
;
3
;
Lubaina Ehsan
3
;
Hans Vitzhum von Eckstaedt
3
;
Barrett Barnes
4
;
Emily McGowan
5
;
Sana Syed
4
and
Donald E. Brown
6
Affiliations:
1
Dept. of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, U.S.A.
;
2
College of Dental Medicine, Columbia University, New York City, NY, U.S.A.
;
3
School of Medicine, University of Virginia, Charlottesville, VA, U.S.A.
;
4
Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, U.S.A.
;
5
Department of Medicine, University of Virginia, Charlottesville, VA, U.S.A.
;
6
School of Data Science, University of Virginia, Charlottesville, VA, U.S.A.
Keyword(s):
Image Segmentation, Eosinophilic Esophagitis, Eosinophils, U-Net, Convolutional Neural Networks.
Abstract:
Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The
diagnostic gold-standard involves manual review of a patient’s biopsy tissue sample by a clinical pathologist
for the presence of 15 or greater eosinophils within a single high-power field (400x magnification). Diagnosing
EoE can be a cumbersome process with added difficulty for assessing the severity and progression of
disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A
U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose
EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial
EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to
find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A
deep imag
e classification model is further applied to discover features other than eosinophils that can be used
to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis
and to provide an automated process for tracking disease severity and progression.
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