Segmentation Quality Assessment

Segmentation quality assessment (SQA) focuses on automatically evaluating the accuracy and reliability of image segmentation results, crucial for deploying AI-based segmentation systems across various domains. Current research emphasizes developing robust SQA methods, often leveraging pre-trained models like Segment Anything Model (SAM) or employing novel architectures that incorporate multi-scale analysis and feature fusion to identify errors like missed or misclassified regions. These advancements are vital for improving the trustworthiness and clinical utility of AI-driven segmentation in applications ranging from medical image analysis to remote sensing, enabling more reliable and efficient workflows.