3D-GAN to generate representative and realistic three-dimensional breast cancer models for virtual clinical trial applications

R. Das; K. Koukoutegos; Y. Wang; M. Keupers; H. Bosmans; K. Houbrechts

doi:10.1117/12.2653561

7 April 2023 3D-GAN to generate representative and realistic three-dimensional breast cancer models for virtual clinical trial applications

R. Das, K. Koukoutegos, Y. Wang, M. Keupers, H. Bosmans, K. Houbrechts

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124632G (2023) https://doi.org/10.1117/12.2653561
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

This paper implements a generative adversarial deep learning network (GAN) to automate the generation of realistic and representative 3D cancer models to investigate digital breast tomosynthesis (DBT) with virtual clinical trials (VCT). Initially, a series of mass lesions from wide-angle DBT cancer cases were manually segmented. We trained a 3D-GAN in two phases: the first phase utilized 105 manually created models of invasive ductal carcinoma (IDA) (including both microlobulated and spiculated lesions) as a training dataset; the second phase focused on enhancing the details of the borders of the GAN-models by using a smaller training set of 42 highly spiculated segmentations. To improve the realism of the generated models in VCTs from both phases, post-processing was carried out by removing disconnected pixels, filling holes, smoothing the borders and elongating existing spicules. Fifteen generated lesions were then simulated in acquired patient images and their realism was validated by a radiologist. 80% of the simulated cases received at least a realism score of 3 out of 5. While the average realism score of the generated voxel models was slightly lower than the average score of manually segmented lesions, the 3D-GAN successfully generated breast cancer models of spiculated masses even when trained with a limited dataset. This same method could be applied to generate other mass models of certain subgroups to allow lesion specific simulations, increasing the efficiency of the process to produce representative lesion models for VCTs.

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R. Das, K. Koukoutegos, Y. Wang, M. Keupers, H. Bosmans, and K. Houbrechts "3D-GAN to generate representative and realistic three-dimensional breast cancer models for virtual clinical trial applications", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124632G (7 April 2023); https://doi.org/10.1117/12.2653561

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