Segmentation Method

Image segmentation, the process of partitioning an image into meaningful regions, is a crucial task across diverse scientific fields and applications, aiming to automate the identification and delineation of objects or features of interest. Current research heavily utilizes deep learning, employing architectures like U-Net, and exploring variations such as incorporating attention mechanisms (e.g., CBAM) or leveraging foundation models like Segment Anything Model (SAM) for improved accuracy and efficiency, particularly in scenarios with limited labeled data. These advancements are significantly impacting fields ranging from medical image analysis (e.g., cancer diagnosis, organ segmentation) to industrial applications (e.g., quality control, material analysis), enabling more accurate and automated analysis of complex visual data.