Observer study-based evaluation of TGAN architecture used to generate oncological PET images

Roberto Fedrigo; Fereshteh Yousefirizi; Ziping Liu; Abhinav K. Jha; Robert V. Bergen; Jean-Francois Rajotte; Raymond T. Ng; Ingrid Bloise; Sara Harsini; Dan J. Kadrmas; Carlos Uribe; Arman Rahmim

doi:10.1117/12.2652661

3 April 2023 Observer study-based evaluation of TGAN architecture used to generate oncological PET images

Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu, Abhinav K. Jha, Robert V. Bergen, Jean-Francois Rajotte, Raymond T. Ng, Ingrid Bloise, Sara Harsini, Dan J. Kadrmas, Carlos Uribe, Arman Rahmim

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Proceedings Volume 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment; 124670S (2023) https://doi.org/10.1117/12.2652661
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack of labeled samples, as well as privacy concerns regarding data sharing. To address these issues, we previously developed (Bergen et al. 2022) a synthetic PET dataset for Head & Neck (H&N) cancer using the temporal generative adversarial network (TGAN) architecture and evaluated its performance segmenting lesions and identifying radiomics features in synthesized images. In this work, a two-alternative forced-choice (2AFC) observer study was performed to quantitatively evaluate the ability of human observers to distinguish between real and synthesized oncological PET images. In the study eight trained readers, including two board-certified nuclear medicine physicians, read 170 real/synthetic image pairs presented as 2D-transaxial using a dedicated web app. For each image pair, the observer was asked to identify the “real” image and input their confidence level with a 5-point Likert scale. P-values were computed using the binomial test and Wilcoxon signed-rank test. A heat map was used to compare the response accuracy distribution for the signed-rank test. Response accuracy for all observers ranged from 36.2% [27.9-44.4] to 63.1% [54.8-71.3]. Six out of eight observers did not identify the real image with statistical significance, indicating that the synthetic dataset was reasonably representative of oncological PET images. Overall, this study adds validity to the realism of our simulated H&N cancer dataset, which may be implemented in the future to train AI algorithms while favoring patient confidentiality and privacy protection.

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Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu, Abhinav K. Jha, Robert V. Bergen, Jean-Francois Rajotte, Raymond T. Ng, Ingrid Bloise, Sara Harsini, Dan J. Kadrmas, Carlos Uribe, and Arman Rahmim "Observer study-based evaluation of TGAN architecture used to generate oncological PET images", Proc. SPIE 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 124670S (3 April 2023); https://doi.org/10.1117/12.2652661

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