Large medical image database impact on generalizability of synthetic CT scan generation.
Authors
Affiliations (3)
Affiliations (3)
- ICUBE, University of Strasbourg, Strasbourg, 67081, France. Electronic address: [email protected].
- iGlobe Scientific SAS, Strasbourg, 67091, France.
- ICUBE, University of Strasbourg, Strasbourg, 67081, France; ICANS Hospital, Strasbourg, 67033, France. Electronic address: [email protected].
Abstract
This study systematically examines the impact of training database size and the generalizability of deep learning models for synthetic medical image generation. Specifically, we employ a Cycle-Consistency Generative Adversarial Network (CycleGAN) with softly paired data to synthesize kilovoltage computed tomography (kVCT) images from megavoltage computed tomography (MVCT) scans. Unlike previous works, which were constrained by limited data availability, our study uses an extensive database comprising 4,000 patient CT scans, an order of magnitude larger than prior research, allowing for a more rigorous assessment of database size in medical image translation. We quantitatively evaluate the fidelity of the generated synthetic images using established image similarity metrics, including Mean Absolute Error (MAE) and Structural Similarity Index Measure (SSIM). Beyond assessing image quality, we investigate the model's capacity for generalization by analyzing its performance across diverse patient subgroups, considering factors such as sex, age, and anatomical region. This approach enables a more granular understanding of how dataset composition influences model robustness.