Semi-supervised image segmentation via selective self-ensembling and boundary uncertainty suppression

Image segmentation plays a key role in many image-guided clinical applications and deep learning technology has proven effective for this task when sufficient labeled images are available. However, it is very time-consuming and labor-intensive to obtain adequate pixel-level image labels. To alleviate the scarcity of labeled images, we propose a novel semi-supervised segmentation method based on the available uncertainty-aware mean teacher (UAMT) framework by introducing two different strategies, i.e., selective self-ensembling (SSE) and boundary uncertainty suppression (BUS). The SSE dynamically selects multiple best student models across different training steps to update the teacher model's weights, while the BUS reduces boundary segmentation errors and improves the quantitative potential of loss functions through a unique uncertainty estimation function. With the two strategies, our proposed method was able to obtain promising segmentation performance with limited labeled images and abundant unlabeled ones. We trained and validated our proposed method by segmenting multiple objects from three public datasets (i.e., PROMISE, REFUGE, and RETA). Extensive experiments showed that our proposed method achieved better segmentation performance than the UAMT, along with the average Dice score (DSC) of 0.7990 for three different objects, and can compete with several existing semi-supervised methods (i.e., HCMT, SASSNet, and DTC). • A novel semi-supervised learning method was developed for accurate image segmentation. • A performance-driven exponential moving average (pEMA) was proposed for semi-supervised learning. • A unique exponential function was proposed for uncertainty estimation. • Extensive segmentation experiments showed the advantage of the developed method.