# Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images

arXiv: https://arxiv.org/abs/2003.07999 · HTML: https://mlsanity.com/paper/2003.07999/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation](https://mlsanity.com/paper/2301.02393/) (2023)
- [Deep vessel segmentation based on a new combination of vesselness filters](https://mlsanity.com/paper/2402.14509/) (2024)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Continuous and complete liver vessel segmentation with graph-attention guided diffusion](https://mlsanity.com/paper/2411.00617/) (2024)
- [Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework](https://mlsanity.com/paper/2504.11469/) (2025)

## Abstract (arXiv)

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.