An unoffical PyTorch implementation of Medical segmentation in 3D and 2D

Pytorch Medical Segmentation

This repository is an unoffical PyTorch implementation of Medical segmentation in 3D and 2D.

Requirements

• pytorch1.7
• python>=3.6

Notice

• You can modify hparam.py to determine whether 2D or 3D segmentation and whether multicategorization is possible.
• We provide algorithms for almost all 2D and 3D segmentation.
• This repository is compatible with almost all medical data formats(e.g. nii.gz, nii, mhd, nrrd, ...), by modifying fold_arch in hparam.py of the config.

Training

• without pretrained-model

set hparam.train_or_test to 'train'
python main.py

• with pretrained-model

set hparam.train_or_test to 'train'
python main.py -k True

Inference

• testing

set hparam.train_or_test to 'test'
python main.py

Examples

Done

• 2D

• [x] unet
• [x] unet++
• [x] miniseg
• [x] segnet
• [x] pspnet
• [x] highresnet(copy from https://github.com/fepegar/highresnet, Thank you to fepegar for your generosity!)
• [x] deeplab
• [x] fcn

• 3D

• [x] unet3d
• [x] densevoxelnet3d
• [x] fcn3d
• [x] vnet3d
• [x] highresnert(copy from https://github.com/fepegar/highresnet, Thank you to fepegar for your generosity!)
• [x] densenet3d

TODO

• [ ] dataset
• [ ] benchmark
• [ ] nnunet

By The Way

This project is not perfect and there are still many problems. If you are using this project and would like to give the author some feedbacks, you can send Kangneng Zhou an email.

GitHub