A Method for Liver Segmentation on Computed Tomography Images in Venous Phase Suitable for Real Environments

Abstract

Nowadays, different methods are being published for the segmentation of the liver but, in general, most of them are not suitable for clinical practice due to several inconveniences as high computational cost, excessive user dependence or low accuracy. The purpose of this paper is to present the performance and validation of a liver segmentation method in computed tomography images (contrast venous phase) where automation, easy user interaction, and low computational cost (besides the required accuracy for clinical purposes) have been taken into account. Firstly, an adaptive filter based on intrinsic parameters of the liver is applied to reduce noise but preserving external liver gradients. In a second step, from a seed or a group of them, voxels with similar intensities are included in an initial 3D mask. Finally, thanks to the combination of morphological operators in different orientations, several non-liver structures (cava vein, ribs, stomach or heart) are removed and the final 3D liver mask is obtained. Thirty public datasets have been used to estimate the accuracy of the proposed algorithm, twenty for training the method and ten for testing it. An average Jaccard index of 0.91 (±0.03), a Hausdorff distance of 26.68 (±10.42) mm, and a runtime of 0.25 seconds per slice, state a promising efficiency and efficacy in the test datasets. To our knowledge, liver segmentation methods in the state of the art are achieving high accuracy at the expense of requiring an exhaustive training stage and so much clinician interaction time in different steps of the process. In this paper, a method based on intensity properties is carried out with a high grade of automatism, an easy user interaction and a low computational cost. The results obtained for different patients state a low variance and a good accuracy in most images, thus the robustness of the method is demonstrated.