Publications/xu.17.icip.inc

Method and datasets

Method

Architecture of the proposed network. We fine tune it and combine linearly fine to coarse feature maps of the pre-trained VGG network. The coarsest feature maps are discarded for the adult images.

Datasets

• Dataset of the MICCAI challenge of Neonatal Brain Segmentation 2012 (NeoBrainS12)
  • Axial images acquired at 40 weeks: 2 training images + 5 test images
  • Coronal images acquired at 30 weeks: 2 training images + 5 test images
  • Coronal images acquired at 40 weeks: 5 test images
• Dataset of the MICCAI challenge of MR Brain Image Segmentation (MRBrainS13)
  • Axial images acquired at 70 years: 5 training images + 15 test images

Illustrations

Experiments

Leave-One-Subject-Out (LOSO) cross-validation on N images + normal training/test experiments. Note that only one training image is used for LOSO 2.

LOSO experiments

Quantitative results of LOSO experiments in terms of Dice coefficient as compared to the state-of-the-art results. The last one is from P. Moeskops et al. on the 15 test images in MRBrainS13 dataset

• Qualitative results on axial images at 40 weeks in NeoBrainS12 dataset

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  Input

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  Segmentation

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  Manual segmentation

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  Difference

• Qualitative results on coronal at 30 weeks in NeoBrainS12 dataset

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• Qualitative results on aging adult at 70 ages in MRBrainS13 dataset

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Neonatal brain MR image segmentation

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Adult brain MR image segmentation

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