Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs
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In this work, we present an empirical study of DCGANs for synthetic generation of fetal head ultrasound, consisting of hyperparameter heuristics and image quality assessment. We present experiments to show the impact of different image sizes, epochs, data size input, and learning rates for quality image assessment on four metrics: mutual information (MI), fréchet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality scores. The resources to reproduce this work are available at <https://github.com/xfetus/miua2022>.
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