Effect of Super Resolution on Low-Resolution MRI Segmentation

抄録

Recently, numerous attempts have been devoted to applying deep-learning-based super resolution to medical images. However, discussions on its usefulness have been limited to the use of indices such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the significance of its application has not been widely discussed. This study aimed to compare several deep learning (DL) —based super-resolution methods using publicly available brain magnetic resonance imaging datasets. The impact of training the segmentation models on super-resolved images was also investigated. The results demonstrated the superiority of the DL-based model over traditional image interpolation methods and the limitations of its application in medical imaging. Additionally, the results indicated that PSNR and SSIM might not always be suitable as evaluation indices.