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NPH Segmentation

Contributors: Fei Xu

This code repository contains Fei's work on the NPH project.

Running the Code

This looks like how you can run inference using a trained model

python3 -W ignore main.py --dataPath '/home/fei/documents/GitHub/NPH_new/data-split/Scans' --betPath '/home/fei/documents/GitHub/NPH_new/data-split/Segmentation' --modelPath 'model_backup/epoch35_2Dresnet3Class_wd6_lr2_2Layer2x2_300.pt' --outputPath 'reconstructed2'

To train the ResNet2Layer2x2_norm_blurnoise:

python3 ResNet2Layer2x2_norm_blurnoise_newdata-Copy1.py

Sample Output from Training

Using cache found in /home/fei/.cache/torch/hub/pytorch_vision_v0.10.0
/home/fei/.local/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Train Epoch: 0 [200/11333 (2%)]	Train Loss: 1.300252 Current accuracy: 44.305%

Skull Strip

bash skull_strip.sh data-split/Scans/Norm_old_005_64yo.nii.gz data-split/skull-strip/Norm_old_005_64yo

Notes

Diff of two files

vimdiff ResNet2Layer2x2_norm_blurnoise_newdata-Copy1.py ResNet1Layer2x2_norm_blurnoise_newdata.py