Mr. Joshua Vanosdol Profile

Joshua Vanosdol

at Oklahoma State Univ

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Proceedings Article | 15 March 2019 Paper

Developing a quantitative ultrasound image feature analysis scheme to assess tumor treatment efficacy using a mouse model

Seyedehnafiseh Mirniaharikandehei, Joshua VanOsdol, Morteza Heidari, Gopichandh Danala, Ashish Ranjan, Bin Zheng

Proceedings Volume 10955, 109551A (2019) https://doi.org/10.1117/12.2512581

KEYWORDS: Tumors, Ultrasonography, Image segmentation, Feature extraction, Image analysis, Computer aided design, Image fusion, Cancer, Mouse models, Computer aided diagnosis and therapy

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In order to improve the efficacy of cancer treatment, many new therapy methods have been proposed and tested. The purpose of this study is to investigate the feasibility and potential advantages of using a low-cost, portable and easy-touse ultrasound imaging modality to quantitatively assess treatment efficacy and/or identify optimal treatment methods. For this purpose, we developed a new interactive computer-aided detection (CAD) scheme based image segmentation and feature analysis scheme, which extracts quantitative image features from ultrasound images of athymic nude mice embedded with tumors. Twenty-three mice were involved in this study and treated using 7 different thermal therapy methods. The longitudinal ultrasound images of mice were taken pre- and post-treatment after 3-days of tumor embedment. A graphic user interface (GUI) of the CAD scheme allows manual segmentation of the tumor regions depicting on the images. Two CAD-computed tumor image feature pools were then established including the features computed from (1) pre-treatment images only and (2) difference between post- and pre-treatment images. Through data analysis, a number of top image features were identified to predict the effectiveness of treatment methods. Pearson Correlation coefficients between two top features selected from above two feature pools versus tumor size increase ratio were 0.373 and 0.552, respectively. Using an equally weighted fusion method of the top two features computed from pre- and post-treatment images, correlation coefficient increased to 0.679. Study results demonstrated the feasibility of extracting a new quantitative imaging marker from ultrasound images to assist in the evaluation of treatment efficacy or tumor response to the treatment.

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