Semi-Supervised Segmentation of Cell Image Stacks for Electron Microscopy

This repository contains source code of Semi-supervised Segmentation for EM images (ISBI 2022).

Getting Started

Install Dependencies

pip install -r requirements.txt

Prepare Dataset

Please place the 3D training image stack and labels in ./data/train_data/ and test image stack and labels in ./dataset/test_data/

Training

Due to memory constraints, we use offline augmentation, as follows:

python _augment.py --stage surpevised

Augmented images and labels are placed in ./dataset/aug/. The number of image slices can be adjusted as needed.

Then, use augmented images to train segmentation network.

python train.py --stage surpevised

Segment the entire training image stack using the trained network.

python inference.py --stage surpevised

The segmentation result is placed in ./data/SEG_result/train_img/

Then, Use MPP:

python image_monography.py

Result is placed in ./data/SEG_result/train_label/

Augment images and labels in ./data/SEG_result/train_img/ and ./data/SEG_result/train_label/

python _augment.py --stage semi-surpevised

The number of Z-axis slices can be adjusted as needed, augmented images and labels are placed in ./dataset/SCM_aug/img and ./dataset/SCM_aug/label

Train SCM:

python train.py --stage semi-surpevised

The scm training is exactly the same as the segmentation network, the only difference is the number of input channels.

Testing

python test_Unet.py

The coarse segmentation result is placed in ./dataset/SEG_result/test_label/stack.tif

Then use:

python test_space_Unet.py

The segmentation result is placed in './data/SCM_result/test_label/stack.tif'