Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
<|reference_start|>Learning Semi-Supervised Medical Image Segmentation from Spatial Registration: Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information -- spatial registration transforms between image volumes. To address this, we propose CCT-R, a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs, CCT-R incorporates two proposed modules: Registration Supervision Loss (RSL) and Registration-Enhanced Positive Sampling (REPS). The RSL leverages segmentation knowledge derived from transforms between labeled and unlabeled volume pairs, providing an additional source of pseudo-labels. REPS enhances contrastive learning by identifying anatomically-corresponding positives across volumes using registration transforms. Experimental results on two challenging medical segmentation benchmarks demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings, with as few as one labeled case. Our code is available at https://github.com/kathyliu579/ContrastiveCross-teachingWithRegistration.<|reference_end|>
@article{liu2024learning,
    title={Learning Semi-Supervised Medical Image Segmentation from Spatial
  Registration},
    author={Qianying Liu, Paul Henderson, Xiao Gu, Hang Dai, Fani Deligianni},
    journal={arXiv preprint arXiv:2409.10422},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10422},
    primaryClass={cs.CV}
}