SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
This work addresses improved super-resolution for medical imaging, which is incremental as it adapts existing INR methods with semantic conditioning.
The authors tackled the problem of medical image super-resolution by proposing SeCo-INR, a framework that conditions implicit neural representations with local semantic priors, achieving higher quantitative scores and more realistic outputs compared to state-of-the-art methods.
Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals without the need for large training datasets. Although INR methods have been studied for medical image super-resolution, their adaptability to localized priors in medical images has not been extensively explored. Medical images contain rich anatomical divisions that could provide valuable local prior information to enhance the accuracy and robustness of INRs. In this work, we propose a novel framework, referred to as the Semantically Conditioned INR (SeCo-INR), that conditions an INR using local priors from a medical image, enabling accurate model fitting and interpolation capabilities to achieve super-resolution. Our framework learns a continuous representation of the semantic segmentation features of a medical image and utilizes it to derive the optimal INR for each semantic region of the image. We tested our framework using several medical imaging modalities and achieved higher quantitative scores and more realistic super-resolution outputs compared to state-of-the-art methods.