A self-supervised framework for improved generalisability in ultrasound B-mode image segmentation

作者：Edward Ellis, Andrew J. Bulpitt, Nasim Parsa, Michael F. Byrne, Sharib Ali · 发表于：Biomedical Signal Processing and Control · 年份：2025 · DOI：10.1016/j.bspc.2025.109089 · 被引用次数：1 · 研究领域：Domain Adaptation and Few-Shot Learning、AI in cancer detection、Ultrasound Imaging and Elastography

Ultrasound (US) imaging is clinically invaluable due to its non-invasive and safe nature. However, interpreting US images is challenging, requires significant expertise, and time, and is often prone to errors. Deep learning offers assistive solutions such as segmentation. Supervised methods rely on large, high-quality, and consistently labelled datasets, which are challenging to curate. Moreover, these methods tend to underperform on out-of-distribution data, limiting their clinical utility. Self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabelled data to enhance model performance and generalisability. We introduce a contrastive SSL approach tailored for B-mode US images, incorporating a novel Relation Contrastive Loss (RCL). RCL encourages learning of distinct features by differentiating positive and negative sample pairs through a learnable metric. Additionally, we propose spatial and frequency-based augmentation strategies for the representation learning on US images. Our approach significantly outperforms traditional supervised segmentation methods across three public breast US datasets, particularly in data-limited scenarios. Notable improvements on the Dice similarity metric include a 4% increase on 20% and 50% of the BUSI dataset, nearly 6% and 9% improvements on 20% and 50% of the BrEaST dataset, and 6.4% and 3.7% improvements on 20% and 50% of the UDIAT dataset, respectively. Furthermore, we demonstrate superior generalisability on...