Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation   Organ segmentation in CT volumes is an important pre-processing step in many
computer assisted intervention and diagnosis methods. In recent years,
convolutional neural networks have dominated the state of the art in this task.
However, since this problem presents a challenging environment due to high
variability in the organ's shape and similarity between tissues, the generation
of false negative and false positive regions in the output segmentation is a
common issue. Recent works have shown that the uncertainty analysis of the
model can provide us with useful information about potential errors in the
segmentation. In this context, we proposed 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 that is solved by training a
graph convolutional network. To test our method we refine the initial output of
a 2D U-Net. We validate our framework with the NIH pancreas dataset and the
spleen dataset of the medical segmentation decathlon. 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, with respect to the original
U-Net's prediction. Finally, we perform a sensitivity analysis on the
parameters of our proposal and discuss the applicability to other CNN
architectures, the results, and current limitations of the model for future
work in this research direction. For reproducibility purposes, we make our code
publicly available at https://github.com/rodsom22/gcn_refinement.

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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph