OpenLiverLab - Open source library for the liver, from images to clinicians

The liver, is our body's largest internal organ and a powerhouse that stores essential vitamins and minerals, performs over 500 metabolic functions, and purifies our blood. Understanding its complex structure is crucial, especially when it comes to diagnosing and treating liver cancer, one of the most common cancers worldwide. For nearly two decades, doctors have been using computer-assisted systems (CAS) to help plan and guide liver cancer treatments like surgery and tumor ablation. These systems improve the accuracy of tumor localization and boost doctors' confidence during procedures. However, creating patient-specific models for these systems is challenging due to the liver's intricate network of vessels and bile ducts. The OpenLiverLab project is a groundbreaking collaboration between two expert teams from University Clermont Auvergne and Oslo University Hospital. This project aims to develop advanced software that can create detailed, personalized liver models. These models will help doctors plan and execute liver cancer treatments more effectively. By combining expertise in biology, computer science, and medical imaging, the OpenLiverLab project hopes to overcome the current challenges in modeling the liver's vascular networks and improve outcomes for liver cancer patients.

The OpenLiverLab project has contributed the consolidation of the combined Slicer-Liver and R-VesselX plugins in 3D Slicer, providing clinicians with a powerful software toolkit for (1) segmentation of liver anatomy, (2) advanced analytics of liver anatomy, including functional separation of the liver and (3) planning of liver therapies. One important result of this project is the verification of cross-compatibility of the software tools in Slicer-Liver and R-VesselX. Currently, the two teams are working on the generation of a dataset resulting from the integration of Slicer-Liver and R-VesselX. Slicer-Liver and R-VesselX are the most comprehensive software extensions for 3D Slicer and the possibility to join them together enables a solid ecosystem for managment of liver cancer therapies research

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.