Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation

Siqi Wu, Chang Chen, Zhiwei Xiong, Xuejin Chen, Xiaoyan Sun. Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation. In MICCAI 2021.

Requirements

Anaconda>=5.2.0 (Python 3.6)
PyTorch>=1.1.0
One or more GPUs with sufficient memory
Memory>=128 GB for data caching

Datasets

To access MitoEM, download files from https://mitoem.grand-challenge.org and convert to arrays.

cd mitoem
python png2npy.py
python tif2npy.py

Otherwise, download .npy files from https://pan.baidu.com/s/1wt1giVGjreYXuArxfOuo1Q (key: us4d).

unzip mitoem-train.zip -d mitoem
unzip mitoem-valid.zip -d mitoem

To access a part of raw images from FAFB and the corresponding labels.

cd fafb-valid/im
cd fafb-valid/seg

Test the pre-trained models

For the reproduction of results listed in Tables 1 and 2.

cd <Name-of-Folder>
python inference.py

Note that, it may take minutes to calculate all of the four metrics.

Train the model

Train an uncertainty-aware model with data from source domain (Rat).

cd u2d-bc-rat-uc-train
python main.py

Generate, rectify, and cache pseudo labels for training.

cd u2d-bc-r2h-train
python inference_4train.py
python generate_mask.py

Train a model with generated labels on target domain (Human).

python main.py