Quantification of photoacoustic microscopy images for ovarian cancer detection

Tianheng Wang; Yi Yang; Umar Alqasemi; Patrick D. Kumavor; Xiaohong Wang; Melinda Sanders; Molly Brewer; Quing Zhu

doi:10.1117/12.2036129

3 March 2014 Quantification of photoacoustic microscopy images for ovarian cancer detection

Tianheng Wang, Yi Yang, Umar Alqasemi, Patrick D. Kumavor, Xiaohong Wang, Melinda Sanders, Molly Brewer, Quing Zhu

Proceedings Volume 8943, Photons Plus Ultrasound: Imaging and Sensing 2014; 894306 (2014) https://doi.org/10.1117/12.2036129
Event: SPIE BiOS, 2014, San Francisco, California, United States

Abstract

In this paper, human ovarian tissues with malignant and benign features were imaged ex vivo by using an opticalresolution photoacoustic microscopy (OR-PAM) system. Several features were quantitatively extracted from PAM images to describe photoacoustic signal distributions and fluctuations. 106 PAM images from 18 human ovaries were classified by applying those extracted features to a logistic prediction model. 57 images from 9 ovaries were used as a training set to train the logistic model, and 49 images from another 9 ovaries were used to test our prediction model. We assumed that if one image from one malignant ovary was classified as malignant, it is sufficient to classify this ovary as malignant. For the training set, we achieved 100% sensitivity and 83.3% specificity; for testing set, we achieved 100% sensitivity and 66.7% specificity. These preliminary results demonstrate that PAM could be extremely valuable in assisting and guiding surgeons for in vivo evaluation of ovarian tissue.

Citation Download Citation

Tianheng Wang, Yi Yang, Umar Alqasemi, Patrick D. Kumavor, Xiaohong Wang, Melinda Sanders, Molly Brewer, and Quing Zhu "Quantification of photoacoustic microscopy images for ovarian cancer detection", Proc. SPIE 8943, Photons Plus Ultrasound: Imaging and Sensing 2014, 894306 (3 March 2014); https://doi.org/10.1117/12.2036129

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