**1. Introduction**

An accurate understanding of liver anatomy is important for surgical safety [1]. This is particularly relevant to the progression of modern surgery toward individualized treatment and the advent of partial hepatectomy and living liver transplantation technology [2, 3]. Initial liver segmentation studies were based on the cadaver liver specimen perfusion model and were limited by the number of specimens and the research techniques available at the time [4–6]. With the development of artificial

intelligence, the development of modern imaging and digital medical research enabled the analysis of dimensional anatomical relationships and spatial vascular variations by three-dimensional (3D) visualization technology from all directions in a transparent and interactive manner [1, 3, 7, 8]. This has particularly helped with the performance of *in vivo* liver segmentation and liver volume measurement [9, 10]. In recent years, several studies have used digital imaging technology for liver segmentation, although these were often confined to liver lobe variations [11–13] and did not involve systematic research. None of the existing liver segmentation methods includes all possible variations in the liver anatomy. The portal vein branches are relatively consistent in the left hepatic lobe, which is divided into segments II, III, and IV. However, none of the existing single segmentation methods describes the different variations in the right liver. We describe a new liver segmentation system based on 3D reconstruction studies of digital liver models.

We used the 3D U-Net framework. In the field of machine learning, the U-Net is a successful encoder-decoder network that has received a lot of attention in recent years. Its encoder part works similarly to a traditional classification CNN in that it successively aggregates semantic information at the expense of reduced spatial information.
