# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
###### Abstract  
Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.  
liver segmentation; liver vessel segmentation; deep learning 3D +
Footnote †: journal: Medicalout as the most relevant for this task. In particular, 3D U-Net captures the volumetric features of organs within medical images. This model is an extension of the 3D FCN [10] (Fully Convolutional Neural network), which suffers from high computational cost and GPU memory consumption. To address these issues, the 3D U-Net architecture was proposed, and is effective even with a limited image dataset.  
To the best of our knowledge, no comparative evaluation has been proposed to develop the best 3D U-Net-based approaches for segmenting the liver volume and hepatic vessels. In general, authors focus either on the liver for future diagnosis analysis (e.g., tumor detection) or on the vessels, without taking the parenchymal volume as a region of interest. However, this question is of high importance for several computer-aided medical applications, involving the Couinaud representation of the liver for instance [11; 12]. As a consequence, we first use an efficient Dense U-Net algorithm in order to segment the liver, so we can then calculate inner vascular extraction. Hence, our study proposes constructing the optimal segmentation pipeline for liver anatomy by comparing the accuracy of three 3D DL architectures: 3D U-Net, 3D Dense U-Net, and 3D MultiRes U-Net [13].  
The paper is organized as follows. First, state-of-the-art works on liver and vessel segmentation are briefly reviewed in Section 2. Then, the description of the 3D setups considered when training/testing the CNN models and the description of the models themselves are given in Sections 3 and 4, respectively. The results of testing the CNN models in the considered setups are presented in Section 5 and discussed in Section 6. Finally, Section 7 concludes the paper.