Metrics to Quantify Global Consistency in Synthetic Medical Images
Daniel Scholz Benedikt Wiestler Daniel Rueckert Martin J. Menten
Image synthesis is increasingly being adopted in medical image processing,
for example for data augmentation or inter-modality image translation. In these
critical applications, the generated images must fulfill a high standard of
biological correctness. A particular requirement for these images is global
consistency, i.e an image being overall coherent and structured so that all
parts of the image fit together in a realistic and meaningful way. Yet,
established image quality metrics do not explicitly quantify this property of
synthetic images. In this work, we introduce two metrics that can measure the
global consistency of synthetic images on a per-image basis. To measure the
global consistency, we presume that a realistic image exhibits consistent
properties, e.g., a person's body fat in a whole-body MRI, throughout the
depicted object or scene. Hence, we quantify global consistency by predicting
and comparing explicit attributes of images on patches using supervised trained
neural networks. Next, we adapt this strategy to an unlabeled setting by
measuring the similarity of implicit image features predicted by a
self-supervised trained network. Our results demonstrate that predicting
explicit attributes of synthetic images on patches can distinguish globally
consistent from inconsistent images. Implicit representations of images are
less sensitive to assess global consistency but are still serviceable when
labeled data is unavailable. Compared to established metrics, such as the FID,
our method can explicitly measure global consistency on a per-image basis,
enabling a dedicated analysis of the biological plausibility of single
synthetic images.