Hepatocellular carcinoma (HCC) is a common type of liver cancer. Its effective diagnosis and monitoring require analyzing computed tomography (CT) scans with intravenous contrast in multiple phases, taken at different intervals post-injection. Organ movement during these intervals, caused by factors like breathing, heartbeat, or patient motion, can affect the accuracy of HCC detection. Aligning two or more scans precisely, especially ensuring the liver’s alignment, is crucial for reconstructing small lesions effectively. Additionally, the presence of various liver lesions, such as active HCC tumors, chemoembolizations, necrosis, portal vein thrombosis, cysts, or other lesions, complicates the diagnosis process. In this paper, we tackle these challenges and propose a deep learning pipeline for detecting, segmenting and ultimately quantifying HCC in multi-phase CT scans. Our rigorous experiments, conducted on a carefully curated dataset from a clinical trial involving HCC patients, demonstrate that our approach not only achieves high-quality detection and segmentation of HCC but also enables fully-automatic, objective, reproducible and accurate response assessment in HCC patients.