Abstract
We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.
Highlights
It is estimated that global cancer incidence and mortality will approximately double during the two decades.[1]
Examples of high-resolution microendoscopy video sequences from the oral data set are shown in Video 1 and Video 2
We developed an automated frame selection algorithm and evaluated its performance relative to manual frame selection using quantitative parameter analysis and quantitative image classification
Summary
It is estimated that global cancer incidence and mortality will approximately double during the two decades.[1] The increase is notable in low-income and middleincome countries, where population growth, aging, and reduced mortality from infectious diseases have led to a steadily increasing cancer burden.[2] Low-income countries often lack effective cancer screening and prevention services. In these settings, conventional diagnostic methods, such as biopsy and histopathology, are limited by the lack of laboratory infrastructure, the lack of trained personnel,[3] and the difficulty of ensuring patient follow-up when test results are not immediately available at the point-of-care. There is a widespread need for rapid, effective methods for early detection of cancer at the point-of-care in low-resource settings
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