Volumetric Segmentation

Volumetric segmentation aims to automatically partition 3D medical images into meaningful regions, such as organs or lesions, aiding diagnosis and treatment planning. Current research heavily focuses on improving accuracy and efficiency through hybrid architectures combining convolutional neural networks (CNNs) and transformers, leveraging the strengths of both for local and global feature extraction. These advancements, often incorporating attention mechanisms and innovative loss functions, are evaluated across diverse datasets and modalities, with a growing emphasis on robustness to noise and adversarial attacks. The resulting improvements in accuracy and efficiency have significant implications for clinical workflows, potentially reducing manual annotation burden and improving diagnostic precision.