Semi-supervised Segmentation of Echocardiography Videos via Noise-resilient Spatiotemporal Semantic Calibration and Fusion

This is the official repo for the paper Semi-supervised Segmentation of Echocardiography Videos via Noise-resilient Spatiotemporal Semantic Calibration and Fusion, published in MedIA.

Due to commercial cooperation considerations, we only provide codes of core modules and the whole trainable models for the convenience of comparisons.

Hopefully, We will release all of the codes in the future.

Requirements

• python=3.8.3
• torch=1.8.0
• CUDA=11.0

Citing SSCF

If you find SSCF useful, please cite our paper.

@article{SSCF,
    title = {Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotemporal semantic calibration and fusion},
    journal = {Medical Image Analysis},
    volume = {78},
    pages = {102397},
    year = {2022},
    issn = {1361-8415},
    doi = {https://doi.org/10.1016/j.media.2022.102397},
    url = {https://www.sciencedirect.com/science/article/pii/S1361841522000494},
    author = {Huisi Wu and Jiasheng Liu and Fangyan Xiao and Zhenkun Wen and Lan Cheng and Jing Qin},
}