AMRSegNet:Adaptive Modality Recalibration Network for Lung Tumor Segmentation on Multi-modal MR Images
This is the Pytorch implementation of AMRSegNet for paper《Adaptive Modality Recalibration Network for Lung Tumor Segmentation on Multi-modal MR Images》.
- Install pytorch with python 3.7, pytorch==1.4.0, torchvision==0.5.0, CUDA==10.1.
- Python package requirement: SimpleITk, pydicom, tensorboardX
- Clone this repository:
git clone https://github.com/Nicholasxin/AMRSegNet
cd AMRSegNet
- Our T2W-DWI MR dataset is private. For code implementation, the dataset for training and testing consist of T2W slices, DWI slices, label slices, which are all paired.
- In the folder of repository
AMRSegNet
, open terminal and runpython train.py
. - Note: adding
--ngpu
to alter to the number of GPUs, adding--batchSz
to change the batch size, adding--nEpochs
to set the number of training epochs. - For showing the training process on tensorboard, the folder
runs
will be created. The trained model will be saved in auto-created folderwork
. - To open the tensorboard, open terminal and run
tensorboard --logdir runs
.
- run
python train.py
with--inference
following the path of inference T2W data,--dwiinference
following the path of inference DWI data,--target
following the path of label of T2W data,--resume
following the path of the best saved training model. All the added commands are requisite.
- 06/01/2021: code released