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.

The 3D Slicer RVXLiverSegmentation plug-in for interactive liver anatomy reconstruction from medical images

“…We first would like to integrate advanced deep learning models for liver and hepatic vessels segmentation (Affane et al, 2021) into our RVXLiverSegmentation plug-in, in order to provide automatic reconstructions that can be then edited by the user with the other tools proposed in the plug-in and in 3D Slicer. Another important work concerns the VMTK module, which needs more adaptations for MRI processing.…”

Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function

“…The application of 3D U-Net [7][8][9][10] is often used for the segmentation of liver vessels from CT images. The 3D U-Net is composed of analytical and synthetical parts [11], similar to the standard U-Net architecture, but it uses 3D operators instead.…”

“…The tests were carried out using the 3D-IRCADb, SLiver007 [12], and private datasets. Affane et al [9] experimented with three distinct 3D U-Net approaches: basic U-Net, MultiRes U-Net, and Dense U-Net. On the 3D-IRCADb dataset, they determined that MultiRes U-Net architecture was superior for segmenting hepatic blood veins.…”