Image style translation for improved imaging of mitral valve surgery training phantoms

With the growing adoption of Competency-Based Medical Education (CBME) in surgical training, there is increasing demand for robust simulation platforms that provide realistic visualization for mitral valve repair procedures. While silicone-based surgical phantoms offer cost-effective training solutions, they lack the visual fidelity of real tissue. This paper presents a comprehensive framework for evaluating and improving the realism of phantom-based surgical simulators through neural style transfer. We introduce a novel patch-based consistency metric for assessing temporal stability in generated images and evaluate multiple GAN architectures including CycleGAN, MUNIT, and DRIT. Our approach is supported by a large-scale dataset comprising 59,411 frames constructed from in vivo surgical procedures and 6,625 phantom-based training images. Results demonstrate that the MUNIT architecture with ImageNet pre-training achieved superior perceptual similarity (FID:16.3±3.2) compared to baseline simulator images (FID: 79.7±17.9), while our patch-based training methodology improved temporal consistency across architectures, reducing mean pixel differences by up to 23.5 %. These findings establish quantitative benchmarks for surgical simulation hyperrealism and provide insights into the fundamental trade-offs between perceptual quality and temporal consistency. Our framework and dataset provide valuable resources for advancing the development of more effective surgical training platforms.