Indroduction

The codes for the work "SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization".

Dataset

1. The dataset we used is ATLAS v1.2[1]. Note that the T1-weighted MR images from 229 patients were through z-score--normalization. [1] Liew S L, Anglin J M, Banks N W, et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations[J]. Scientific data, 2018, 5(1): 1-11.
2. The dataset is firstly processed according to this to get train.h5 file.
3. Then, the dataset is processed to get three .npy files. python zscore.py

Environment

Name	Version
Python	3.7
pytorch	1.7.0
torch	1.8.0
numpy	1.21.5
pandas	1.3.5

Train/Test

python train.py

The model parameters (trained on all sites from ATLAS v1.2 except for Site 5) are available on this.