Enhancing the automatic segmentation and analysis of 3D liver vasculature models
Yassine Machta, Omar Ali, Kevin Hakkakian, Ana Vlasceanu, Amaury Facque, Nicolas Golse, Irene Vignon-Clementel
TL;DR
The paper tackles the challenge of automatic 3D liver vessel segmentation and the identification of portal and hepatic venous trees for surgical planning and morphometric analysis. It investigates differentiable skeletonization methods, notably integrating NeuralSkel with ClDice-based losses, within an nnUNet framework, and compares them against traditional skeletonization. A dual-label separation pipeline is introduced to disentangle portal and hepatic trees from single-label vessel segmentations, and a downstream anatomical labeling with morphometric analysis is developed using Lee skeletonization, Sknw, and NetworkX, augmented by Couinaud segmentation. The authors release the LIRCAD dataset (77 cases) and demonstrate that the separation pipeline reduces annotation burden and improves segmentation performance, while NeuralSkel improves centerline quality but yields only modest gains in overall segmentation. Overall, the work enables automatic, anatomy-aware vessel labeling and first morphometric analyses, with potential to support surgical planning and liver disease research.
Abstract
Surgical assessment of liver cancer patients requires identification of the vessel trees from medical images. Specifically, the venous trees - the portal (perfusing) and the hepatic (draining) trees are important for understanding the liver anatomy and disease state, and perform surgery planning. This research aims to improve the 3D segmentation, skeletonization, and subsequent analysis of vessel trees, by creating an automatic pipeline based on deep learning and image processing techniques. The first part of this work explores the impact of differentiable skeletonization methods such as ClDice and morphological skeletonization loss, on the overall liver vessel segmentation performance. To this aim, it studies how to improve vessel tree connectivity. The second part of this study converts a single class vessel segmentation into multi-class ones, separating the two venous trees. It builds on the previous two-class vessel segmentation model, which vessel tree outputs might be entangled, and on connected components and skeleton analyses of the trees. After providing sub-labeling of the specific anatomical branches of each venous tree, these algorithms also enable a morphometric analysis of the vessel trees by extracting various geometrical markers. In conclusion, we propose a method that successfully improves current skeletonization methods, for extensive vascular trees that contain vessels of different calibers. The separation algorithm creates a clean multi-class segmentation of the vessels, validated by surgeons to provide low error. A new, publicly shared high-quality liver vessel dataset of 77 cases is thus created. Finally a method to annotate vessel trees according to anatomy is provided, enabling a unique liver vessel morphometry analysis.