  With the injection of contrast material into blood vessels, multi-phase
contrasted CT images can enhance the visibility of vessel networks in the human
body. Reconstructing the 3D geometric morphology of liver vessels from the
contrasted CT images can enable multiple liver preoperative surgical planning
applications. Automatic reconstruction of liver vessel morphology remains a
challenging problem due to the morphological complexity of liver vessels and
the inconsistent vessel intensities among different multi-phase contrasted CT
images. On the other side, high integrity is required for the 3D reconstruction
to avoid decision making biases. In this paper, we propose a framework for
liver vessel morphology reconstruction using both a fully convolutional neural
network and a graph attention network. A fully convolutional neural network is
first trained to produce the liver vessel centerline heatmap. An
over-reconstructed liver vessel graph model is then traced based on the heatmap
using an image processing based algorithm. We use a graph attention network to
prune the false-positive branches by predicting the presence probability of
each segmented branch in the initial reconstruction using the aggregated CNN
features. We evaluated the proposed framework on an in-house dataset consisting
of 418 multi-phase abdomen CT images with contrast. The proposed graph network
pruning improves the overall reconstruction F1 score by 6.4% over the baseline.
It also outperformed the other state-of-the-art curvilinear structure
reconstruction algorithms.
