# 3D PETCT Tumor Lesion Segmentation via GCN Refinement

arXiv: https://arxiv.org/abs/2302.12571 · HTML: https://mlsanity.com/paper/2302.12571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Dual Graph Convolutional Network for Semantic Segmentation](https://mlsanity.com/paper/1909.06121/) (2019)
- [A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs](https://mlsanity.com/paper/1908.08588/) (2019)
- [Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation](https://mlsanity.com/paper/2007.14728/) (2020)
- [Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images](https://mlsanity.com/paper/2102.04799/) (2021)
- [Graph-FCN for image semantic segmentation](https://mlsanity.com/paper/2001.00335/) (2020)
- [Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks](https://mlsanity.com/paper/2103.16762/) (2021)
- [An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation](https://mlsanity.com/paper/2012.03352/) (2020)
- [Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation](https://mlsanity.com/paper/2108.03117/) (2021)
- [Graph Neural Network and Superpixel Based Brain Tissue Segmentation (Corrected Version)](https://mlsanity.com/paper/2209.12764/) (2022)

## Abstract (arXiv)

Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as over-segmentation and under-segmentation. Therefore, to address such issues, we propose a post-processing method based on a graph convolutional neural network (GCN) to refine inaccurate segmentation parts and improve the overall segmentation accuracy. Firstly, nnUNet is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certainty and uncertainty nodes establish the nodes of a graph neural network. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected for uncertain nodes to form edges. The highly uncertain nodes are taken as the subsequent refinement targets. Secondly, the nnUNet result of the certainty nodes is used as label to form a semi-supervised graph network problem, and the uncertainty part is optimized through training the GCN network to improve the segmentation performance. This describes our proposed nnUNet-GCN segmentation framework. We perform tumor segmentation experiments on the PET/CT dataset in the MICCIA2022 autoPET challenge. Among them, 30 cases are randomly selected for testing, and the experimental results show that the false positive rate is effectively reduced with nnUNet-GCN refinement. In quantitative analysis, there is an improvement of 2.12 % on the average Dice score, 6.34 on 95 % Hausdorff Distance (HD95), and 1.72 on average symmetric surface distance (ASSD). The quantitative and qualitative evaluation results show that GCN post-processing methods can effectively improve tumor segmentation performance.