Three-Dimensional CT Image Segmentation by Combining 2 D Fully Convolutional Network with 3 D Majority Voting

@inproceedings{Zhou2016ThreeDimensionalCI,
  title={Three-Dimensional CT Image Segmentation by Combining 2 D Fully Convolutional Network with 3 D Majority Voting},
  author={Xiangrong Zhou and Takaaki Ito and Ryosuke Takayama and Song Wang and Takeshi Hara and Hiroshi Fujita},
  year={2016}
}
We propose a novel approach for automatic segmentation of anatomical structures on 3D CT images by voting from a fully convolutional network (FCN), which accomplishes an end-to-end, voxel-wise multiple-class classification to map each voxel in a CT image directly to an anatomical label. The proposed method simplifies the segmentation of the anatomical structures (including multiple organs) in a CT image (generally in 3D) to majority voting for the semantic segmentation of multiple 2D slices… CONTINUE READING

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First trial and evaluation of anatomical structure segmentations in 3D CT images based only on deep learning

  • X. Zhou, T. Ito, R. Takayama, S. Wang, T. Hara, H. Fujita
  • Medical Image and Information Sciences
  • 2016
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