Medical imaging powered by artificial intelligence (AI) can help doctors make proper treatment decisions, plan surgeries for organ transplants, and help oncologists improve treatment plans for cancer patients who are diagnosed early. CT or MR images could be used to detect cancer in its early stages, which would prevent millions of deaths worldwide. The purpose of this research is to present a deep learning-based algorithm that can assist radiologists in planning liver transplantations and cancer detection by segmenting liver images from abdominal CT and MR images. The proposed model was trained on a standard dataset of CHAOS challenge for CT, and MRI images. Our model has accurately performed segmentation of the liver from the abdominal images with a dice coefficient of 0.983 on CT images and a dice coefficient of 0.935 on MRI T1 Out-phase modality

Liver cancer, segmentation, CT, MRI, deep-learning, artificial intelligence