CGFLRVE: Context-guided future liver remnant volume estimation using artificial intelligence models

• CentraleSupeléc (France)

• Clínica Santa María (Chile)

• CONICET (Argentina)

• OPIS project-team, Saclay Inria Centre, Inria (France)

• LITIS Lab (France)

• Universidad de Los Andes (Chile)

• Universidad de Santiago (USACH) (Chile)

Automatic Liver-Segmentation is an essential task in the medical context. Current AIbased models are focused on liver and tumor segmentation, that is not enough for surgical planning, especially for liver metastases. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images. However, for surgery, a more challenging task is required. In this context, it is critical to estimate accurately the remnant liver volume after resection; for instance, in patients with liver metastases. Estimating the future liver remnant is a challenging task because the type of surgery to be performed depends on each patient’s clinical setting, the center’s experience, number and location of liver lesions, among others. This means that future liver remnant segmentation depends on the patient’s clinical context. Therefore, the goal of this project is to design, implement and evaluate fine-grained liver segmentation guided by the context that allows us to precisely estimate remnant liver volume. Our work is guided by five objectives: (1) evaluate SOTA liver segmentation models, including the recent published architecture HybridGNet; (2) design and evaluate models for fine-grained liver segmentation models taking into account models like SAM and HybridGNet; (3) estimate remnant liver volume using the fine-grained liver segmentation model; (4) Integrate contextual information by prompts for liver segmentation. Finally, we present results on public and private datasets. For the private case, we collaborate with a local health center, which provides us access to data. To accomplish the proposed objectives, we have formed a multidisciplinary team, including physicians with specialization in radiology and experts on computer vision applied to medical images.

In France: 

• Fannia Pacheco, researcher, CGFLRVE coordinator, LITIS Lab

• Caroline Petitjean, researcher, CGFLRVE coordinator, LITIS Lab

• Maria Vakalopoulou, researcher, CGFLRVE coordinator, OPIS project-team, Saclay Inria Centre, Inria

In Chile: 

• Violeta Chang, researcher, CGFLRVE coordinator, Universidad de Santiago

• Héctor Henríquez, researcher, CGFLRVE coordinator, Clínica Santa María

• José Saavedra, researcher, CGFLRVE coordinator, Universidad de Los Andes

• Aline Xavier, researcher, Universidad de Santiago

In Argentina: 

• Enzo Ferrante, Enzo, researcher, CGFLRVE coordinator, CONICET