3D CoordConv Segmentation

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Grand Challenge 2017 Multi-Modality Whole Heart Segmentation

  • http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/

Contribution

  • An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution [https://arxiv.org/abs/1807.03247]

Training Run

In code directory

> python main.py --params=ct_train.json

Result

Model Background MLV LABC LVBC RABC RVBC ASA PUA Average DSC
U-net 3D 0.995 0.918 0.929 0.912 0.925 0.923 0.843 0.923 0.909
U-net 3D + CoordConv 0.995 0.919 0.926 0.912 0.933 0.924 0.928 0.897 0.920
  • MLV: the Myocardium of the left ventricle, LABC: the left atrium blood cavity, LVBC: the left ventricle blood cavity, RABC: the right atrium blood cavity, RVBC: the right ventricle blood cavity, ASA: the ascending aorta, PUA: the pulmonary artery
  • Average DSC is average of classes that excluded background
placeholder image 1 placeholder image 2 placeholder image 3
Left: Mask, Middle: U-Net 3D, Right: U-Net 3D CoordConv

Details

Data Number of train set Number of validation set Patch dim Resize rate Batch size Epochs Number of train patch image Number of validation patch image Metric Loss function Optimizer Learning rate Number of GPU
CT 18 2 96 0.7 2 100 20 100 Dice Similarity Coefficient dice coefficient loss Adam 0.0001 4

Limit

The host server is down, so the test set can no longer be evaluated.