Parameter-free dual-student ensemble for semi-supervised ischemic stroke segmentation on follow-up NCCT

Abstract

Non-contrast computed tomography (NCCT) is widely used for post-treatment monitoring of patients with ischemic stroke due to its rapid acquisition, low cost, and non-invasiveness. Segmentation of ischemic lesions on NCCT images is critical for assessing disease severity and guiding clinical decision-making. We propose an ensemble-based semi-supervised segmentation (ESS) framework to reduce the dependence of convolutional neural networks (CNNs) on a large number of manual annotations. The proposed method mainly consists of two components: first, strong data augmentation is introduced to increase sample diversity and alleviate overfitting in the presence of limited labeled data; second, a parameter-free ensemble module is adopted to generate more stable supervision signals for unlabeled samples and improve the reliability of pseudo-labels by fusing the predictions of the two student models. The proposed ESS is evaluated on a manually annotated follow-up NCCT dataset consisting of 9,020 image slices. ESS consistently achieves the highest Dice coefficient among all compared methods under all four labeled-data ratios, with a more pronounced performance advantage when labeled data are scarce. These results demonstrate the effectiveness of ESS for semi-supervised segmentation of follow-up NCCT under limited annotation conditions.