AlbertHuyb/VesicleSegmentation

2D Vesicle Instance Segmentation

Main framework of training and inference code comes from C-3-Framework. The original liscense is included under ./liscense

Follow C-3-Framework
- Python 3.x
- Pytorch 1.0 (some networks only support 0.4): http://pytorch.org .
- other libs in requirements.txt, run pip install -r requirements.txt.
- Perhaps some packages like h5py need to be installed after above command. pip install h5py

cd ./data
Modify settings in ./data/config.py
Run python 0_augment.py to do augmentation manually.
Run python 1_data_prepared.py to split sugmented data into train, valid, test datasets.

Modify settings in ./training/config.py
cd ./training
python train.py
Results will be stored at ./training/exp/. Tensorboard can be used to visualize the result by tensorboard --logdir=exp --port=6006

cd ./inference

Synapse locations to input images and masks.
- By python 0_synapse_gen.py
- Output to OUTPUT_DIR defined in ./code/config.py, including:
  - dir full: full input image
  - dir part: masked input image (not used)
  - dir mask: binary mask
Use trained model to predict the heat map.
- By python 1_test_all.py.
- Predict with MODEL_DIR/MODEL_NAME directed by ./code/config.py. Output to OUTPUT_DIR/result, including:
  - dir pred: heat map in npy format
  - dir mask: predicted mask by 0.3 threshold
  - dir gt: empty
Multiply the predicted mask and heat map with input mask.
- By python 2_multiply.py
- Output to OUTPUT_DIR/mask_result, including:
  - dir pred: heat map in npy format
  - dir mask: predicted binary mask by 0.3 threshold.
Use watershed to cut heat map into vesicles. (Two-step Watershed)
- By python 3_post_process.py
- Output to OUTPUT_DIR/wshed_result, including:
  - dir data: segmentations in npy format
  - dir img: matplotlib images of segmentation result
Analyze the final 3D bounding box of synapse seg and prepare to display in VAST.
- By python 4_final_step.py
- Output to OUTPUT_DIR/vast_volume