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Analytics for computation and visualization of liver resections


Description:

Liver cancer is one of the most common types of cancer and its incidence is increasing. Surgical resection is the only curative treatment for some types of cancer. For nearly two decades, surgeons have been employing computer-assisted planning systems (CAPS); these systems show an increase of precision in surgical planning and an improved orientation and confidecne of the surgeon during operation. Despite these benefits, CAPS have found difficulties to Reach the clinical practice (the most noticeable is the problems to generate 3D patient-specific models from images). With the introduction of AI in medical imaging, these problems have been greatly reduced. This has created a new scenario where 3D patient-specific models are going to be systematically generated for its use in surgical planning and guidance. This new reality is perfect for the introduction of liver analytics and AI in surgical planning for the improvement of liver surgery practice. The main problems that can benefit from the introduction of AI and liver analytics are: (1) the difficulties for generation of resection plans in difficult cases (e.g., multiple metastases)—this proces is still manual—; (2) the standard division of the liver in segments (largely used for resectio planning) does not pose a wide consensus in the medical community, and therefore, there is the need to investigate new methods that can computationally generate different types of vascular territories; and (3) there are no formal methods to specify and communicate resection plans—clinicians are currently using subjective descriptions (written or oral), hand-drawings and pictures taken from the surgery, and therefore, there is a need for investigating visualization techniques able to capture the critical information contained in a resection plan in a formal way that can be interpreted by any clinical expert. This project will develop algorithms to solve these problems using analtyics, geometric modeling, visualization and AI.


Project leader: Rafael Palomar

Started: 2020

Ends: 2024

Category: Helse Sør-Øst RHF

Sector: Helseforetak

Budget: 15048000

Institution: OSLO UNIVERSITETSSYKEHUS HF

Address: Oslo