Large Scale TotalSegmentator Dataset

The TotalSegmentator dataset is a large-scale, publicly available collection of annotated medical images used to train and evaluate deep learning models for automated anatomical segmentation. Current research focuses on improving segmentation accuracy and robustness across various imaging modalities (CT, MRI) and anatomical structures, often employing nnU-Net architectures and exploring techniques like zero-shot learning and model scaling to enhance performance. This resource significantly advances medical image analysis by providing a standardized benchmark for evaluating segmentation algorithms and facilitating the development of tools for applications such as organ volumetry, disease characterization, and surgical planning.