Liver CT

Liver CT research focuses on improving the accuracy and efficiency of analyzing liver CT scans for diagnosis and treatment planning. Current efforts concentrate on developing advanced deep learning models, such as UNets and GANs, often incorporating multi-modal fusion techniques and attention mechanisms to enhance segmentation and lesion detection. These advancements aim to improve the speed and accuracy of tasks like organ segmentation, lesion identification, and even genomic mutation prediction from CT images, ultimately leading to better patient care and streamlined clinical workflows. The development of robust, automated tools is particularly important for applications such as radiotherapy planning and follow-up scans.