Simulation of spiculated breast lesions

Premkumar Elangovan; Faisal Alrehily; R. Ferrari Pinto; Alaleh Rashidnasab; David R. Dance; Kenneth C. Young; Kevin Wells

doi:10.1117/12.2216227

22 March 2016 Simulation of spiculated breast lesions

Premkumar Elangovan, Faisal Alrehily, R. Ferrari Pinto, Alaleh Rashidnasab, David R. Dance, Kenneth C. Young, Kevin Wells

Author Affiliations +

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97832E (2016) https://doi.org/10.1117/12.2216227
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Virtual clinical trials are a promising new approach increasingly used for the evaluation and comparison of breast imaging modalities. A key component in such an assessment paradigm is the use of simulated pathology, in particular, simulation of lesions. Breast mass lesions can be generally classified into two categories based on their appearance; nonspiculated masses and spiculated masses. In our previous work, we have successfully simulated non-spiculated masses using a fractal growth process known as diffusion limited aggregation. In this new work, we have extended the DLA model to simulate spiculated lesions by using features extracted from patient DBT images containing spiculated lesions. The features extracted included spicule length, width, curvature and distribution. This information was used to simulate realistic looking spicules which were attached to the surface of a DLA mass to produce a spiculated mass. A batch of simulated spiculated masses was inserted into normal patient images and presented to an experienced radiologist for review. The study yielded promising results with the radiologist rating 60% of simulated lesions in 2D and 50% of simulated lesions in DBT as realistic.

Citation Download Citation

Premkumar Elangovan, Faisal Alrehily, R. Ferrari Pinto, Alaleh Rashidnasab, David R. Dance, Kenneth C. Young, and Kevin Wells "Simulation of spiculated breast lesions", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97832E (22 March 2016); https://doi.org/10.1117/12.2216227

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