Inter Slice

Inter-slice processing in medical imaging focuses on improving the quality and analysis of volumetric data by addressing the inherent limitations of anisotropic resolution, where resolution differs significantly between slices and within slices. Current research emphasizes developing deep learning models, including transformer-based architectures and implicit neural representations, to enhance inter-slice connectivity and resolution, often employing techniques like attention mechanisms and self-supervised learning to overcome data scarcity. These advancements are crucial for improving the accuracy of medical image segmentation, super-resolution, and denoising, ultimately leading to more reliable diagnoses and better-informed clinical decisions.