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.
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.
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
- Axial 40 weeks in NeoBrainS12 dataset
- Coronal 30 weeks in NeoBrainS12 dataset
- Aging adult at 70 ages in MRBrainS13 dataset
Neonatal brain MR image segmentation
Adult brain MR image segmentation