Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

@article{Setio2016PulmonaryND,
  title={Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks},
  author={Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Geert J. S. Litjens and Paul K. Gerke and Colin Jacobs and Sarah J. van Riel and Mathilde M. W. Wille and Matiullah Naqibullah and Clara I. S{\'a}nchez and Bram van Ginneken},
  journal={IEEE Transactions on Medical Imaging},
  year={2016},
  volume={35},
  pages={1160-1169}
}
We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple… CONTINUE READING

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Key Quantitative Results

  • On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively.

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References

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Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks

A.A.A. Setio, F. Ciompi, +7 authors B. van Ginneken
  • IEEE Transactions on Medical Imaging, 2016. 2
  • 2016
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