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%.

METHODS: In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries.

RESULTS: The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets.

METHOD: The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments.

RESULTS: The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy.