3D U Net

3D U-Net architectures are convolutional neural networks used primarily for three-dimensional image segmentation tasks, aiming for accurate and efficient delineation of structures within volumetric data. Current research focuses on improving 3D U-Net performance through architectural modifications (e.g., incorporating transformers, attention mechanisms, and multi-resolution approaches), loss function optimization, and advanced training strategies like active learning and data augmentation. These advancements have significant implications across diverse fields, including medical image analysis (e.g., brain tumor, kidney, and lung segmentation) and other areas like weather prediction and 3D object generation, enabling more precise and robust automated analysis of complex 3D data.