Semi-supervised auto-segmentation method for pelvic organ-at-risk in magnetic resonance images based on deep-learning

CONCLUSION: The outcomes of our study demonstrate that it is possible to train a multi-OAR segmentation model using small annotation samples and additional unlabeled data. To effectively annotate the dataset, ensemble learning and post-processing methods were employed. Additionally, when dealing with anisotropy and limited sample sizes, the 2D model outperformed the 3D model in terms of performance.PMID:38386963 | DOI:10.1002/acm2.14296
Source: Journal of Applied Clinical Medical Physics - Category: Physics Authors: Source Type: research