Automated animation pipeline for visualizing in silico tumor growth models

Andrea S. Kim; Aunnasha Sengupta; Aldo Badano

doi:10.1117/12.2654988

7 April 2023 Automated animation pipeline for visualizing in silico tumor growth models

Andrea S. Kim, Aunnasha Sengupta, Aldo Badano

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 1246343 (2023) https://doi.org/10.1117/12.2654988
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

While there is increased interest in medical and scientific computational modeling tools for generating in silico medical datasets, tools for visualizing the volumetric data present with hurdles for those without previous experience in graphics rendering. We describe an open-source automatedworkflowto visualize volumetric computational medical imaging datasets with a focus on cancer lesion growth models. Simulated raw data for the growth of a tumor were generated at 50 time points using a previously described growth algorithm that considers the surrounding anatomy to affect tumor morphology. The voxelized models were converted to the VDB volume format for rendering using an automated Python script within the software Houdini. The visualization of volume data allows for detailed inspection and improved understanding of the spatial configuration of the tumor and surrounding anatomy affecting the growth.

Citation Download Citation

Andrea S. Kim, Aunnasha Sengupta, and Aldo Badano "Automated animation pipeline for visualizing in silico tumor growth models", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246343 (7 April 2023); https://doi.org/10.1117/12.2654988

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