Deep-Learning-Based Image Super-resolution of an End-Expandable Optical Fiber Probe for Application in Esophageal Cancer Diagnostics

Authors

Xiaohui Zhang
Mimi Tan
Mansour Nabil
Richa Shukla
Shaleen Vasavada
Sharmila Anandasabapathy
Mark A Anastasio
Elena Petrova

Publication Date

4-1-2024

Journal

Journal of Biomedical Optics

DOI

10.1117/1.JBO.29.4.046001

PMID

38585417

PMCID

PMC10993061

PubMedCentral® Posted Date

4-4-2024

PubMedCentral® Full Text Version

Post-Print

Published Open-Access

yes

Keywords

Humans, Deep Learning, Optical Fibers, Esophageal Neoplasms, Barrett Esophagus, Image Processing, Computer-Assisted, end-expandable optical fiber probe, deep-learning-based super-resolution, degradation model, Barrett’s esophagus, esophageal cancer, microendoscopy, endomicroscopy

Abstract

SIGNIFICANCE: Endoscopic screening for esophageal cancer (EC) may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving specificity, the limited field of view (

AIM: To improve the efficiency of endoscopic screening, we propose a novel concept of end-expandable endoscopic optical fiber probe for larger field of visualization and for the first time evaluate a deep-learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability.

APPROACH: To demonstrate feasibility of the end-expandable optical fiber probe, DL-SR was applied on simulated low-resolution microendoscopic images to generate super-resolved (SR) ones. Varying the degradation model of image data acquisition, we identified the optimal parameters for optical fiber probe prototyping. The proposed screening method was validated with a human pathology reading study.

RESULTS: For various degradation parameters considered, the DL-SR method demonstrated different levels of improvement of traditional measures of image quality. The endoscopists' interpretations of the SR images were comparable to those performed on the high-resolution ones.

CONCLUSIONS: This work suggests avenues for development of DL-SR-enabled sparse image reconstruction to improve high-yield EC screening and similar clinical applications.

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