Publications/xu.17.icip.inc

From LRDE

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. Xu.17.icip-pepeline.png

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

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. Xu.17.icip-experiments.jpg

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. Xu.17.icip-losoresults.jpg

  • Qualitative results on axial images at 40 weeks in NeoBrainS12 dataset
  • Qualitative results on coronal at 30 weeks in NeoBrainS12 dataset
  • Qualitative results on aging adult at 70 ages in MRBrainS13 dataset

Neonatal brain MR image segmentation

Xu.17.icip-axial40results.jpg


Xu.17.icip-coronal30results.jpg


Xu.17.icip-coronal40results.jpg


Adult brain MR image segmentation

Xu.17.icip-adult70results.jpg