CT Volume

CT volume analysis focuses on extracting meaningful information from three-dimensional computed tomography scans, primarily aiming to automate tasks like organ segmentation, lesion detection, and report generation to assist radiologists. Current research emphasizes developing and benchmarking deep learning models, including U-Net variations, transformers, and diffusion models, for improved accuracy and efficiency in these tasks, often leveraging large, multi-center datasets for training and validation. This work holds significant potential for improving diagnostic accuracy, reducing radiologist workload, and accelerating the development of personalized medicine approaches through more efficient and reliable image analysis.