Volumetric Medical Image Segmentation

Volumetric medical image segmentation aims to automatically identify and delineate anatomical structures within 3D medical scans, facilitating accurate diagnosis and treatment planning. Current research emphasizes improving segmentation accuracy and efficiency through hybrid architectures combining convolutional neural networks (CNNs) and transformers, as well as exploring techniques like dynamic inference, self-supervised learning (including masked autoencoders and contrastive learning), and efficient weight initialization. These advancements are crucial for addressing challenges like limited annotated data, computational cost, and robustness to adversarial attacks, ultimately improving the reliability and accessibility of medical image analysis tools.