MRI Slice

MRI slice analysis focuses on extracting meaningful information from individual 2D slices of 3D MRI scans, aiming to improve diagnostic accuracy and efficiency across various medical applications. Current research emphasizes automated segmentation techniques using deep learning models like U-Nets, convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs) to analyze features within slices and even synthesize missing data or improve resolution. These advancements are crucial for streamlining tasks such as body composition analysis, brain tumor detection, and dementia diagnosis, ultimately leading to faster and more accurate clinical workflows. Furthermore, research explores methods to harmonize data across multiple sites and scanners, improving the reliability and generalizability of findings.