Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation
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Yichi Zhang, Rushi Jiao, Qingcheng Liao, Dongyang Li, Jicong Zhang
2022
Abstract
Medical image segmentation is a fundamental and critical step in many
clinical approaches. Semi-supervised learning has been widely applied to
medical image segmentation tasks since it alleviates the heavy burden of
acquiring expert-examined annotations and takes the advantage of unlabeled data
which is much easier to acquire. Although consistency learning has been proven
to be an effective approach by enforcing an invariance of predictions under
different distributions, existing approaches cannot make full use of
region-level shape constraint and boundary-level distance information from
unlabeled data. In this paper, we propose a novel uncertainty-guided mutual
consistency learning framework to effectively exploit unlabeled data by
integrating intra-task consistency learning from up-to-date predictions for
self-ensembling and cross-task consistency learning from task-level
regularization to exploit geometric shape information. The framework is guided
by the estimated segmentation uncertainty of models to select out relatively
certain predictions for consistency learning, so as to effectively exploit more
reliable information from unlabeled data. Experiments on two publicly available
benchmark datasets showed that: 1) Our proposed method can achieve significant
performance improvement by leveraging unlabeled data, with up to 4.13% and
9.82% in Dice coefficient compared to supervised baseline on left atrium
segmentation and brain tumor segmentation, respectively. 2) Compared with other
semi-supervised segmentation methods, our proposed method achieve better
segmentation performance under the same backbone network and task settings on
both datasets, demonstrating the effectiveness and robustness of our method and
potential transferability for other medical image segmentation tasks.
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