Analytics for computation and visualization of liver resections

For nearly two decades computers have been helping surgeons doing more precise surgery. Like if it was a video game, liver surgeons will use 3D virtual liver models generated from real patients’ data, not only to see the location and size of liver tumors, but also to plan the surgical strategy that best suits the patient (interactively manipulating these models). Thanks to artificial intelligence, these 3D models can now be created in a faster and more automatic way. In ALive, we are developing new possibilities to use 3D models for making better surgical plans: How can we make 3D interactive tools so surgeons can "design" in 3D the best surgical plan? Could the computer learn how a surgeon "designs" a plan and create one by its own? While 3D virtual environments can help humans to understand geometric relationships between 3D objects (like organs and tumors), for some purposes, we prefer to have the information simplified. Consider a map, which is as a simplification of a 3D world, which allows people to quickly find useful information. Similarly, in ALive we want to create simplifications of the 3D virtual resection plans in 2D diagrams that can help experts to interpret a resection plan; 2D diagrams as opposed to 3D models can be used for reporting resections in medical records and documents. To achieve these goals, in the ALive project we are creating many 3D models from real patients, and together with surgeons, designing the best tools for them to perform resection plans in 3D. Hence, at the same time as we provide useful 3D virtual tools to surgeons, we collect data on how a surgeons plans resections. This information is further used to train artificial intelligence algorithms to learn the process and try to reproduce it. At the same time, we can use the resections to study the best way to simplify the resection in 2D diagrams.

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.