Improving Deep Learning Based Liver Vessel Segmentation Using Automated Connectivity Analysis

Segmenting full vessel systems of the human liver is important for many applications in liver surgery and intervention planning. While methods exist for training DNNs for vessel segmentation, no method we know of efficiently extracts the vessel graph without modifying the DNN architecture. We demonstrate a fully automatic method for extracting and separating vessel graphs directly from the output of a segmentation model by applying a modified algorithm for vessel connectivity analysis. This method significantly improves the centerline sensitivity of reconstructed graphs on the IRCAD dataset and achieves similar scores for splitting vessel systems as the recently published TopNet approach.