Presentation + Paper
9 March 2018 Realistic lesion simulation: application of hyperelastic deformation to lesion-local environment in lung CT
Author Affiliations +
Abstract
Lesion simulation programs can be used to insert realistic, generated lesions into anatomical images for further study. Most lesion simulation programs rely on insertion of a mask within an otherwise unchangeable, i.e., static surrounding—in reality, lesions deform their immediate surroundings. The goal of the current study was to develop a lesion model based on realistic morphology, but with additional hyperelastic modification of the lesion-local environment in accordance with lesion morphology and location. Physical displacement of the existing tissue was modeled by finite element application of hyperelastic theory to a lung tissue segmentation, incorporating the material properties for both parenchymal and stromal tissue. An observer study was conducted with the data generated from this model to ascertain the realism of hyperelastic and static lesion insertions compared to real lesions. The comparisons were characterized in terms of the area under the ROC curve, AUC. The results indicate that observers are less able to distinguish between hyperelastically-inserted lesions and real ones (AUC=0.62) compared to statically-inserted lesions (AUC=0.75). The findings indicate that hyperelastic deformation offers an improvement in the realism of simulated lesions in CT imaging.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas J. Sauer, Ehsan Abadi, Justin Solomon, Jocelyn M. Hoye, and Ehsan Samei "Realistic lesion simulation: application of hyperelastic deformation to lesion-local environment in lung CT", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731U (9 March 2018); https://doi.org/10.1117/12.2294962
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KEYWORDS
Tissues

Lung

Image segmentation

Computed tomography

Finite element methods

Visualization

Data modeling

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