Corpus ID: 237491534

A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation

  title={A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation},
  author={Camillo F Saueressig and Adam Berkley and Reshma Munbodh and Ritambhara Singh},
We present a joint graph convolution – image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model each brain as a graph composed of distinct image regions, which is initially segmented by a graph neural network (GNN). Subsequently, the tumorous volume identified by the GNN is further refined by a simple (voxel) convolutional neural network (CNN), which produces the final segmentation. This approach captures both global brain feature… Expand

Figures and Tables from this paper


Exploring Graph-Based Neural Networks for Automatic Brain Tumor Segmentation
This project represents 3D MRI scans of the brain as a graph, where different regions in the images are represented by nodes and edges connect adjacent regions, and applies several variations of GNNs for the automatic segmentation of brain tumors from MRI scans. Expand
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
The set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences are reported, finding that different algorithms worked best for different sub-regions, but that no single algorithm ranked in the top for all sub-Regions simultaneously. Expand
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks. Expand
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
This set of labels and features should enable direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as performance evaluation of computer-aided segmentation methods. Expand
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor’s molecular characterization, in pre-operative baseline mpMRI data from 2,000 patients. Expand
Graph Neural Networks: A Review of Methods and Applications
A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed. Expand
Deep learning approaches to biomedical image segmentation
In this review, the basics of deep learning methods are discussed along with an overview of successful implementations involving image segmentation for different medical applications and the future need for further improvements is pointed out. Expand
Semi-Supervised Classification with Graph Convolutional Networks
A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin. Expand
Artificial intelligence in cancer imaging: Clinical challenges and applications
The authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types to illustrate how common clinical problems are being addressed. Expand
Inductive Representation Learning on Large Graphs
GraphSAGE is presented, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data and outperforms strong baselines on three inductive node-classification benchmarks. Expand