• Corpus ID: 174802485

An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation

@article{SoberanisMukul2019AnUG,
  title={An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation},
  author={Roger D. Soberanis-Mukul and Shadi Albarqouni and Nassir Navab},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.02191}
}
Organ segmentation is an important pre-processing step in many computer assisted intervention and computer assisted diagnosis methods. In recent years, CNNs have dominated the state of the art in this task. Organ segmentation scenarios present a challenging environment for these methods due to high variability in shape, similarity with background, etc. This leads to the generation of false negative and false positive regions in the output segmentation. In this context, the uncertainty analysis… 
View PDF on arXiv
4 Citations
Highly Influential Citations
1
Background Citations
1
Methods Citations
1

Figures and Tables from this paper

4 Citations

Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation
TLDR
According to the experimental results on pancreas segmentation from Computed Tomography images, improvement in the quantitative measures is demonstrated and the proposed method shows qualitative enhancements in the segmentation maps, as demonstrated in the visual results.
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
TLDR
This survey thoroughly review the different types of graph architectures and their applications in healthcare in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis.
Light-weight shadow detection via GCN-based annotation strategy and knowledge distillation
Effective integration of object boundaries and regions for improving the performance of medical image segmentation by using two cascaded networks.

References

SHOWING 1-10 OF 46 REFERENCES
Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement
TLDR
A segmentation refinement method based on uncertainty analysis and graph convolutional networks is proposed that outperforms the state-of-the art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen, with respect to the original U-Net's prediction.
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
TLDR
A novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images that can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations.
Integrating 3D Geometry of Organ for Improving Medical Image Segmentation
TLDR
Experimental results show that the proposed network can not only output accurate segmentation, but also generate smooth 3D mesh simultaneously which can be used for further 3D shape analysis.
Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections
TLDR
This method improves the accuracy of a state-of-the-art fully convolutional semantic segmentation approach on the publicly available COCO and PASCAL datasets, and it shows significantly better results on selected sequences of the finely-annotated DAVIS dataset.
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
TLDR
This paper forms a fixed-point model which uses a predicted segmentation mask to shrink the input region and outperform the state-of-the-art by more than \(4\%\), measured by the average Dice-Sorensen Coefficient (DSC).
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs
TLDR
An end-to-end deep learning segmentation method by combining a 3D UNet architecture with a graph neural network (GNN) model with two types of graph adjacency, which is applied to the task of segmenting the airway tree from chest CT scans.
Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation
TLDR
A semi-supervised learning approach, in which a segmentation network is trained from both labelled and unlabelled data, which outperforms a state-of-the-art multi-atlas segmentation method by a large margin and the speed is substantially faster.
Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆
Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation
TLDR
PaNN achieves state-of-the-art performance on the MICCAI2015 challenge ``Multi-Atlas Labeling Beyond the Cranial Vault'', a competition on organ segmentation in the abdomen, and is reformulated as a min-max form and optimized via the stochastic primal-dual gradient algorithm.
...
...