Abdominal CT

Abdominal CT image analysis focuses on automatically segmenting and identifying various organs and structures within the abdomen from computed tomography scans, aiding in diagnosis and treatment planning. Current research emphasizes developing robust and accurate segmentation models, often employing 3D convolutional neural networks (CNNs) and incorporating techniques like self-supervised learning, federated learning, and attention mechanisms (e.g., Vision Transformers) to address challenges such as data scarcity, label inconsistencies, and computational efficiency. These advancements are crucial for improving the speed and accuracy of medical image analysis, ultimately leading to better patient care and accelerating research in various clinical applications.