Lymphoma Segmentation

Lymphoma segmentation, the automated identification and delineation of lymphoma lesions in medical images, aims to improve diagnostic accuracy and treatment planning. Current research focuses on developing and refining deep learning models, including U-Net variations, transformer-based architectures, and novel approaches incorporating spatial and frequency domain information, to accurately segment lymphomas from various imaging modalities like PET/CT and ultrasound. These advancements leverage multi-modal data fusion and advanced techniques like cross-attention mechanisms to overcome challenges posed by lesion heterogeneity and unclear boundaries, ultimately improving the speed and consistency of lymphoma assessment. The resulting improvements in segmentation accuracy have significant implications for clinical practice, enabling more precise staging, treatment monitoring, and ultimately, better patient outcomes.