Microstructure Segmentation

Microstructure segmentation aims to automatically identify and delineate distinct regions within microscopic images, crucial for analyzing materials, biological tissues, and other complex systems. Current research heavily utilizes deep learning, employing architectures like U-Net, Transformers, and GANs, often combined with techniques like semantic boosting and transfer learning to improve accuracy and efficiency, particularly when dealing with limited data. These advancements enable more precise quantitative analysis of microstructures, impacting fields ranging from materials science (predicting material properties) to medical imaging (diagnosing diseases) by providing detailed, automated characterization of complex structures.