Rethinking Perceptual Metrics for Medical Image Translation
Nicholas Konz Yuwen Chen Hanxue Gu Haoyu Dong Maciej A. Mazurowski
Modern medical image translation methods use generative models for tasks such
as the conversion of CT images to MRI. Evaluating these methods typically
relies on some chosen downstream task in the target domain, such as
segmentation. On the other hand, task-agnostic metrics are attractive, such as
the network feature-based perceptual metrics (e.g., FID) that are common to
image translation in general computer vision. In this paper, we investigate
evaluation metrics for medical image translation on two medical image
translation tasks (GE breast MRI to Siemens breast MRI and lumbar spine MRI to
CT), tested on various state-of-the-art translation methods. We show that
perceptual metrics do not generally correlate with segmentation metrics due to
them extending poorly to the anatomical constraints of this sub-field, with FID
being especially inconsistent. However, we find that the lesser-used
pixel-level SWD metric may be useful for subtle intra-modality translation. Our
results demonstrate the need for further research into helpful metrics for
medical image translation.