Automated airway segmentation by learning graphical structure

• URL: http://arxiv.org/abs/2109.14792v1
• Date: Thu, 30 Sep 2021 01:37:31 GMT
• Title: Automated airway segmentation by learning graphical structure
• Authors: Yihua Yang
• Abstract summary: We put forward an advanced method for airway segmentation based on the existent convolutional neural network (CNN) and graph neural network (GNN) The proposed model shows that compared with the CNN-only method, the combination of CNN and GNN has a better performance in that the bronchi in the chest CT scans can be detected in most cases.
• Score: 0.76146285961466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this research project, we put forward an advanced method for airway segmentation based on the existent convolutional neural network (CNN) and graph neural network (GNN). The method is originated from the vessel segmentation, but we ameliorate it and enable the novel model to perform better for datasets from computed tomography (CT) scans. Current methods for airway segmentation are considering the regular grid only. No matter what the detailed model is, including the 3-dimensional CNN or 2-dimensional CNN in three directions, the overall graph structures are not taken into consideration. In our model, with the neighbourhoods of airway taken into account, the graph structure is incorporated and the segmentation of airways are improved compared with the traditional CNN methods. We perform experiments on the chest CT scans, where the ground truth segmentation labels are produced manually. The proposed model shows that compared with the CNN-only method, the combination of CNN and GNN has a better performance in that the bronchi in the chest CT scans can be detected in most cases. In addition, the model we propose has a wide extension since the architecture is also utilitarian in fulfilling similar aims in other datasets. Hence, the state-of-the-art model is of great significance and highly applicable in our daily lives. Keywords: Airway segmentation, Convolutional neural network, Graph neural network

Related papers

• CNN2GNN: How to Bridge CNN with GNN [59.42117676779735]
  We propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. The performance of distilled boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
  arXiv  Detail & Related papers  (2024-04-23T08:19:08Z)
• Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification [0.0]
  We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements.
  arXiv  Detail & Related papers  (2023-07-24T13:39:21Z)
• Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study [0.0]
  This paper proposes a CNN algorithm for the Alamosa Canyon Bridge as a real structure. Three different CNN models were considered to predict one and two malfunctioned sensors. The accuracy of the model was increased by adding a convolutional layer.
  arXiv  Detail & Related papers  (2022-04-11T23:24:03Z)
• A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network [3.5665681694253903]
  Detection of hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks. In this paper, an ensemble of CNN-based approaches is presented to overcome some problems like high variance during prediction, overfitting problem and also prediction errors.
  arXiv  Detail & Related papers  (2022-02-25T06:46:58Z)
• Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images [0.342658286826597]
  We present a synthesis of Recurrent CNN, Residual Network and Dense Convolutional Network based on the U-Net model architecture. The proposed model tested on the benchmark Lung Lesion dataset showed better performance on segmentation tasks than its equivalent models.
  arXiv  Detail & Related papers  (2021-02-01T06:34:10Z)
• Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction [57.74609918453932]
  Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. We propose a hybrid representation learning approach to address this challenge.
  arXiv  Detail & Related papers  (2020-12-14T05:22:49Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study [3.072491397378425]
  This paper identifies a variety of components that increase the performance of CNN-based algorithms for COVID-19 grading from CT images. A 3D CNN with these components achieved an area under the ROC curve (AUC) of 0.934 on our test set of 105 CT scans and an AUC of 0.923 on a publicly available set of 742 CT scans.
  arXiv  Detail & Related papers  (2020-09-21T09:58:57Z)
• LGNN: A Context-aware Line Segment Detector [53.424521592941936]
  We present a novel real-time line segment detection scheme called Line Graph Neural Network (LGNN) Our LGNN employs a deep convolutional neural network (DCNN) for proposing line segment directly, with a graph neural network (GNN) module for reasoning their connectivities. Compared with the state-of-the-art, LGNN achieves near real-time performance without compromising accuracy.
  arXiv  Detail & Related papers  (2020-08-13T13:23:18Z)
• Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition [57.98278794950759]
  Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data. We present a novel spatial-temporal GCN architecture which is defined via the Poincar'e geometry. We evaluate our method on two current largest scale 3D datasets.
  arXiv  Detail & Related papers  (2020-07-30T18:23:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.