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

Materials

Trained models

The trained models and corresponding files for training for the proposed method on NeoBrainS12 and MRBrainS13 datasets are available in the following:

• Training on Axial images at 40 weeks in NeoBrainS12 dataset are available in this archive
• Training on coronal images at 30 weeks in NeoBrainS12 dataset are available in this archive
• Training on previous training sets for coronal images at 40 weeks in NeoBrainS12 dataset are available in this archive
• Training on MRBrainS13 dataset is available in this archive

Segmentation results

The pre-computed segmentation results of the proposed method on NeoBrainS12 and MRBrainS13 datasets are available in the following:

• Results on Axial images at 40 weeks in NeoBrainS12 dataset are available in this archive
• Results on coronal images at 30 weeks in NeoBrainS12 dataset are available in this archive
• Results on coronal images at 40 weeks in NeoBrainS12 dataset are available in this archive
• Results on MRBrainS13 dataset are available in this archive

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.

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

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• Some results on coronal images at 30 weeks in NeoBrainS12 dataset

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• Some results on axial images of aging adults at 70 ages in MRBrainS13 dataset

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Neonatal brain MR image segmentation

• Results on axial images at 40 weeks in NeoBrainS12 dataset. More details can be found Here

Some result images

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• Results on coronal images at 30 weeks in NeoBrainS12 dataset. More details can be found Here

Some result images (some small errors inside the red circle)

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• Results on coronal images at 40 weeks in NeoBrainS12 dataset. More details can be found Here

Some result images (some small errors inside red circles)

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Adult brain MR image segmentation

• Results on aging adult images at 70 years in MRBrainS13 dataset. Only top 10 methods among 38 submitted ones are shown. More results and details can be found Here

Some result images

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