Official PyTorch implementation of RedoxSegNet described in the paper.
Kideog Bae, Muzaffer Özbey, Alexander Ho, Edita Aksamitiene, Kevin K Tan, Janet E Sorrells, Rishyashring R Iyer, Mark A. Anastasio, Stephen A. Boppart "Label-free Segmentation of Mitochondria for Simultaneous Morphological and Metabolic Studies"
python>=3.6.9
torch>=1.7.1
torchvision>=0.8.2
cuda=>11.2
ninja
python3.x-dev (apt install, x should match your python3 version, ex: 3.8)
- Clone this repo:
git clone https://github.com/comp-imaging-sci/RedoxSegNet
cd RedoxSegNet You should structure your aligned dataset in the following way:
/dataset_path/
│ ├── train
| - 1.tif
| - 2.tif
| - ...
│ ├── val
│ └── test
python train_ddgan_translation_microscopy.py --data_dir /folder/path/for/data --image_size 256 --exp exp_RedoxSegNet --num_channels 2 --num_channels_dae 64 --ch_mult 1 1 2 2 4 4 --num_timesteps 4 --num_res_blocks 2 --batch_size 8 --num_epoch 200 --ngf 64 --embedding_type positional --use_ema --r1_gamma 2. --z_emb_dim 256 --lr_d 1e-4 --lr_g 2e-4 --lazy_reg 10 --num_process_per_node 1 --save_content --checkpoint_dir /checkpoints/runs --input_selection --input_channels 0 --out_channels 3 --local_rank 0
| Argument | Description |
|---|---|
--data_dir |
Root directory path of the dataset folder. |
--exp |
Name of the experiment, used for storing model checkpoints. |
--image_size |
Size of image. |
--num_channels |
Number of channels for generator input(summation of input and target channel) |
--num_timesteps |
Number of diffusion steps. |
--checkpoint_dir |
Directory where model checkpoints are saved. |
--lr_d |
Initial learning rate for the optimizer of discriminator. |
--lr_g |
Initial learning rate for the optimizer of generator. |
--input_selection |
Indicate the selection of specific channels in source data. |
--input_channels |
Channel index of source data, list with space (--input_channels 0 1 3). |
--out_channels |
Channel index of target data, list with space (--out_channels 3 4). |
--local_rank |
GPU selection. |
python test_ddgan_translation_inference.py --data_dir /folder/path/for/test/data --result_dir /folder/path/for/result/ --image_size 256 --num_channels 2 --num_channels_dae 64 --ch_mult 1 1 2 2 4 4 --num_timesteps 4 --num_res_blocks 2 --batch_size 8 --input_selection --input_channels 0 --num_target_channels 1 --checkpoint_model /checkpoints/runs/exp_RedoxSegNet/netG_100.pth
| Argument | Description |
|---|---|
--data_dir |
Root directory path of the dataset folder. |
--result_dir |
Directory where results are saved. |
--image_size |
Size of image. |
--num_channels |
Number of channels for generator input(summation of input and target channel) |
--num_timesteps |
Number of diffusion steps. |
--input_selection |
Indicate the selection of specific channels in source data. |
--input_channels |
Channel index of source data, list with space (--input_channels 0 1 3). |
--num_target_channels |
Number of channels in target image. |
This code uses libraries from SynDiff repository.