Hepatic vessels segmentation using deep learning and preprocessing enhancement

Abstract

Purpose: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.

Methods: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.

Results: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.

Conclusions: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

Keywords: CED; U-net; abdominal CT; deep learning; residual block; vasculature segmentation.

FIGURE 1

_{(a) Original vessels appearance versus (b) enhanced vessels appearance.}

FIGURE 2

Different training patches with different sizes.

FIGURE 3

The proposed FCN.

FIGURE 4

_{(a) Residual block, (b) dense block, and (c) ResDense block.}

FIGURE 5

_{Liver vasculature segmentation for different datasets.}