For nearly two decades, surgeons and interventional radiologists have employed computer-assisted systems (CAS). These systems have the ability to support the decision-making process for planning and guiding liver cancer therapies like surgical resection, tumor ablation or trans-arterial chemo-embolization (TACE). CAS have shown not only to improve the tumor localization, but also to increase the orientation and the confidence of the physician during the therapy. However, CAS heavily rely on the use of patient specific models that are difficult to obtain and analyze. In the liver, particularly, the complexity lies in its complicated network of vessels and bile ducts. Investigating human vascular networks has been a source of various multidisciplinary research implying biology, physiology, mathematics, computer science, among others. The increasing evolution of computer science in this research has raised the interest in numerically modeling vessels. Despite numerous developments in this field, computer extraction and modeling of vascular networks from images is still an open challenge. The goal of the OpenLiverLab project is to consolidate a workflow comprising the analysis and generation of patient-specific liver models (segmentation, geometric modeling and perfusion modeling) to support planning and guidance of liver cancer therapies (resection and ablation). This is a new cooperation between two complementary teams (UCA and OUS) that will enable the development of novel softwares dedicated to clinicians. Two major clinical applications are targeted, and comply with current research topics for the liver: hepatic surgery and TACE. This project benefits from the results obtained by 2 national fundings: the R-Vessel-X project - Robust vascular network extraction and understanding within hepatic biomedical images and the ALive project - Analytics for computation and visualization of liver resections.