Label Fusion

Label fusion is a technique that combines multiple segmentation labels or predictions to improve the accuracy and robustness of image segmentation models, particularly in scenarios with limited or noisy data. Current research focuses on developing efficient label fusion algorithms, including graph-coloring methods for reducing computational complexity and consensus-based approaches for collaborative learning across multiple datasets. This technique is particularly valuable in medical image analysis, where it addresses challenges like inter-rater variability and data scarcity, leading to more reliable and accurate segmentations for diagnosis and treatment planning. Furthermore, label fusion is being explored in other domains, such as improving the quality of semantic segmentation in video sequences.