Body Composition

Body composition analysis, focusing on quantifying skeletal muscle, visceral and subcutaneous fat, is crucial for assessing health risks and conditions like obesity and sarcopenia. Current research emphasizes developing automated segmentation methods using deep learning models (like nnUNet and others) applied to various imaging modalities (MRI, CT), improving accuracy and efficiency compared to manual segmentation. These advancements, including open-source tools like Comp2Comp, are enhancing the accessibility and reliability of body composition assessment for both research and clinical applications, leading to more personalized health management. The integration of multimodal data, such as facial images, with traditional anthropometric measures further refines estimation accuracy.