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Real-time fluorescence target/background (T/B) ratio calculation in multimodal endoscopy for detecting GI tract cancer
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Paper Abstract

Multimodal endoscopy, with fluorescence-labeled probes binding to overexpressed molecular targets, is a promising technology to visualize early-stage cancer. T/B ratio is the quantitative analysis used to correlate fluorescence regions to cancer. Currently, T/B ratio calculation is post-processing and does not provide real-time feedback to the endoscopist. To achieve real-time computer assisted diagnosis (CAD), we establish image processing protocols for calculating T/B ratio and locating high-risk fluorescence regions for guiding biopsy and therapy in Barrett’s esophagus (BE) patients.

Methods: Chan-Vese algorithm, an active contour model, is used to segment high-risk regions in fluorescence videos. A semi-implicit gradient descent method was applied to minimize the energy function of this algorithm and evolve the segmentation. The surrounding background was then identified using morphology operation. The average T/B ratio was computed and regions of interest were highlighted based on user-selected thresholding. Evaluation was conducted on 50 fluorescence videos acquired from clinical video recordings using a custom multimodal endoscope.

Results: With a processing speed of 2 fps on a laptop computer, we obtained accurate segmentation of high-risk regions examined by experts. For each case, the clinical user could optimize target boundary by changing the penalty on area inside the contour.

Conclusion: Automatic and real-time procedure of calculating T/B ratio and identifying high-risk regions of early esophageal cancer was developed. Future work will increase processing speed to <5 fps, refine the clinical interface, and apply to additional GI cancers and fluorescence peptides.

Paper Details

Date Published: 15 February 2017
PDF: 9 pages
Proc. SPIE 10057, Multimodal Biomedical Imaging XII, 100570D (15 February 2017); doi: 10.1117/12.2250234
Show Author Affiliations
Yang Jiang, Univ. of Washington (United States)
Yuanzheng Gong, Univ. of Washington (United States)
Thomas D. Wang, Univ. of Michigan (United States)
Eric J. Seibel, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 10057:
Multimodal Biomedical Imaging XII
Fred S. Azar; Xavier Intes, Editor(s)

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