Segmentation Pipeline

Segmentation pipelines automate the process of partitioning images into meaningful regions, a crucial step in various fields like medical imaging and materials science. Current research emphasizes improving accuracy and robustness, particularly addressing challenges like topological inconsistencies, limited training data (few-shot learning), and handling diverse image modalities (e.g., combining CT and MRI). Common approaches involve deep learning architectures such as U-Nets, attention mechanisms, and generative adversarial networks (GANs), often integrated with pre-processing steps (e.g., super-resolution) and post-processing refinement to enhance segmentation quality and address specific application needs. These advancements enable more efficient and accurate analysis across diverse domains, impacting areas such as medical diagnosis, surgical planning, and materials characterization.