Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics
Xiaohui Zhang, Mimi Tan, Mansour Nabil, Richa Shukla, Shaleen, Vasavada, Sharmila Anandasabapathy, Mark A. Anastasio, Elena Petrova

TL;DR
This paper introduces a novel end-expandable optical fiber probe combined with deep learning-based super-resolution to enhance field of view and image quality in esophageal cancer screening, potentially enabling earlier detection.
Contribution
It proposes a new end-expandable fiber probe and applies deep learning super-resolution to improve imaging, addressing limited field of view in microendoscopy.
Findings
DL-SR improves image quality across various degradation models
Super-resolved images are comparable to high-resolution images in clinical interpretation
The proposed method enhances screening efficiency for esophageal cancer
Abstract
Significance: Endoscopic screening for esophageal cancer may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving specificity, the limited field of view (<1 mm) significantly reduces the ability to survey large areas efficiently in esophageal cancer screening. Aim: To improve the efficiency of endoscopic screening, we proposed a novel end-expandable endoscopic optical fiber probe for larger field of visualization and employed 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 (LR) microendoscopic images to generate super-resolved (SR) ones. Varying the degradation model of image data acquisition, we identified the optimal parameters for…
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Taxonomy
TopicsPhotoacoustic and Ultrasonic Imaging · Esophageal Cancer Research and Treatment · Lung Cancer Diagnosis and Treatment
