Journal of Biomedical Optics
Thayer School of Engineering
Geisel School of Medicine
An automated algorithm and methodology is presented to identify tumor-tissue morphologies based on broadband scatter data measured by raster scan imaging of the samples. A quasi-confocal reflectance imaging system was used to directly measure the tissue scatter reflectance in situ, and the spectrum was used to identify the scattering power, amplitude, and total wavelength-integrated intensity. Pancreatic tumor and normal samples were characterized using the instrument, and subtle changes in the scatter signal were encountered within regions of each sample. Discrimination between normal versus tumor tissue was readily performed using a K-nearest neighbor classifier algorithm. A similar approach worked for regions of tumor morphology when statistical preprocessing of the scattering parameters was included to create additional data features. This type of automated interpretation methodology can provide a tool for guiding surgical resection in areas where microscopy imaging cannot be realized efficiently by the surgeon. In addition, the results indicate important design changes for future systems.
Garcia-Allende PB, Krishnaswamy V, Hoopes PJ, Samkoe KS, Conde OM, Pogue BW. Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures. J Biomed Opt. 2009 May-Jun;14(3):034034. doi: 10.1117/1.3155512. PMID: 19566327; PMCID: PMC2857335.
Dartmouth Digital Commons Citation
Garcia-Allende, Pilar Beatriz; Krishnaswamy, Venkataramanan; Hoopes, P Jack; Samkoe, Kimberley S.; Conde, Olga M.; and Pogue, Brian W., "Automated Identification of Tumor Microscopic Morphology based on Macroscopically Measured Scatter Signatures" (2009). Dartmouth Scholarship. 3637.