Two‐step artificial intelligence algorithm for liver segmentation automates anatomic virtual hepatectomy

Abstract

BackgroundAnatomic virtual hepatectomy with precise liver segmentation for hemilivers, sectors, or Couinaud's segments using conventional three‐dimensional simulation is not automated and artificial intelligence (AI)‐based algorithms have not yet been applied.MethodsComputed tomography data of 174 living‐donor candidates for liver transplantation (training data) were used for developing a new two‐step AI algorithm to automate liver segmentation that was validated in another 51 donors (validation data). The Pure‐AI (no human intervention) and ground truth (GT, full human intervention) data groups were compared.ResultsIn the Pure‐AI group, the median Dice coefficients of the right and left hemilivers were highly similar, 0.95 and 0.92, respectively; sectors, posterior to lateral: 0.86–0.92, and Couinaud's segments 1–8: 0.71–0.89. Labeling of the first‐order branch as hemiliver, right or left portal vein perfectly matched; 92.8% of the second‐order (sectors); 91.6% of third‐order (segments) matched between the Pure‐AI and GT data.ConclusionsThe two‐step AI algorithm for liver segmentation automates anatomic virtual hepatectomy. The AI‐based algorithm correctly divided all hemilivers, and more than 90% of the sectors and segments.