Poor Segmentation

Poor segmentation in image analysis, particularly in medical imaging and remote sensing, hinders accurate object identification and quantification. Current research focuses on improving segmentation accuracy by leveraging pre-trained models (like UNet and transformer architectures), incorporating multi-scale feature fusion and attention mechanisms, and developing novel loss functions that better capture shape and spatial information. These advancements aim to address challenges such as imprecise boundary delineation, handling of varying object scales and orientations, and the need for efficient quality assurance in large-scale datasets, ultimately improving the reliability and applicability of automated image analysis across diverse fields.