Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for
AF remain suboptimal due to a lack of understanding of the underlying atrial structures that …

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively
exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ …

Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative
atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper …

Recent trends in semi-supervised learning have significantly boosted the performance of 3D
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

Learning-based methods suffer from a deficiency of clean annotations, especially in
biomedical segmentation. Although many semi-supervised methods have been proposed to …

Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing
the cost of manual annotation of clinicians by using unlabelled data, when developing …

Automated and accurate segmentations of left atrium (LA) and atrial scars from late
gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand …

Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …