• 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… 

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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).
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.
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.
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