Functional Segmentation for Preoperative Liver Resection Based on Hepatic Vascular Networks

Abstract

Successful liver resection relies on accurate estimation of future liver remnant volume (FLRV). According to Couinaud’s scheme, a liver is composed of eight functionally independent segments, each of which has its own vascular in- and out-flow tracks. Segmenting a liver by this scheme is vital to postoperative regeneration and hence prognosis outcome. Conventionally, estimation of liver segments was often done by hand on 3D computed tomography. The process is generally tedious, time consuming, and prone to observer variability. Alternatively, computerized methods had been proposed but impeded by anatomically irrelevant approximation and manually specified markers. To resolve the issues, this paper presents a novel method for functional liver segmentation. Its main contribution was performing analyses of differential geometry directly on a liver surface and interior venous system. Except for a few points being placed on major vessels, anatomical references required for defining all separating surfaces were automatically identified. To demonstrate its merits, virtual liver resection was implemented on the standard MICCAI SLIVER07 dataset, and the resultant segments were benchmarked against four most related works. Visual and numerical assessments reported herein indicated that our method could faithfully label all Couinaud’s segments, especially the caudate, with lesser degree of user interaction. The preliminary findings suggested that it can be integrated into augmented surgical planning and intervention.