Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging

• Published in: Journal of medical imaging (Bellingham, Wash.) Wash.), 2023-03, Vol.10 (2), p.024007-024007
• Main Authors: Hooper, Sarah M., Wu, Sen, Davies, Rhodri H., Bhuva, Anish, Schelbert, Erik B., Moon, James C., Kellman, Peter, Xue, Hui, Langlotz, Curtis, Ré, Christopher
• Format: Article
• Language: English
• ISSN: 2329-4302; 2329-4310
• DOI: 10.1117/1.JMI.10.2.024007

Neural networks have potential to automate medical image segmentation but require expensive labeling efforts. While methods have been proposed to reduce the labeling burden, most have not been thoroughly evaluated on large, clinical datasets or clinical tasks. We propose a method to train segmentation networks with limited labeled data and focus on thorough network evaluation. We propose a semi-supervised method that leverages data augmentation, consistency regularization, and pseudolabeling and train four cardiac magnetic resonance (MR) segmentation networks. We evaluate the models on multiinstitutional, multiscanner, multidisease cardiac MR datasets using five cardiac functional biomarkers, which are compared to an expert's measurements using Lin's concordance correlation coefficient (CCC), the within-subject coefficient of variation (CV), and the Dice coefficient. The semi-supervised networks achieve strong agreement using Lin's CCC ( ), CV similar to an expert, and strong generalization performance. We compare the error modes of the semi-supervised networks against fully supervised networks. We evaluate semi-supervised model performance as a function of labeled training data and with different types of model supervision, showing that a model trained with 100 labeled image slices can achieve a Dice coefficient within 1.10% of a network trained with 16,000+ labeled image slices. We evaluate semi-supervision for medical image segmentation using heterogeneous datasets and clinical metrics. As methods for training models with little labeled data become more common, knowledge about how they perform on clinical tasks, how they fail, and how they perform with different amounts of labeled data is useful to model developers and users.