Tomography Segmentation

Tomography segmentation aims to automatically identify and delineate specific structures or regions of interest within tomographic images (e.g., CT, PET, MRI, cryo-ET), a task crucial for medical diagnosis and biological research. Current research heavily utilizes deep learning, employing architectures like U-Net and its variants (e.g., SwinUNETR, DynUNet), often incorporating attention mechanisms and model ensembling to improve accuracy and robustness, even with limited training data or challenging image qualities. These advancements accelerate analysis, improve diagnostic accuracy, and enable more efficient processing of large datasets across various imaging modalities, impacting fields from oncology to structural biology.