全心分割基础模型：利用多模态医学影像中的师生学习
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging
AI总结：
提出基于师生自监督学习的全心分割基础模型，利用xLSTM骨干和多模态预训练，减少标注依赖并提升CT/MRI分割精度。
摘要：
从CT和MRI扫描中进行全心分割对于心血管疾病分析至关重要，然而现有方法面临模态特定偏差以及对大量标注数据集的需求。为解决这些挑战，我们提出了一种基于师生架构的自监督学习（SSL）框架的全心分割基础模型。我们的模型在大型无标注CT和MRI扫描数据集上进行预训练，利用xLSTM骨干网络捕获3D医学图像中的长程空间依赖和复杂解剖结构。通过整合多模态预训练，我们的方法确保在CT和MRI两种模态上具有强大的泛化能力，减轻模态特定变化并在多样临床环境中提高分割准确性。大规模无标注数据的使用显著减少了对人工标注的依赖，即使在有限标注数据下也能实现稳健性能。我们进一步引入基于xLSTM-UNet的架构用于下游全心分割任务，并在少标签CT和MRI数据集上展示了其有效性。我们的结果验证了所提模型的稳健性和适应性，突显了其在推进医学影像自动化全心分割方面的潜力。
英文摘要：
Whole-heart segmentation from CT and MRI scans is crucial for cardiovascular disease analysis, yet existing methods struggle with modality-specific biases and the need for extensive labeled datasets. To address these challenges, we propose a foundation model for whole-heart segmentation using a self-supervised learning (SSL) framework based on a student-teacher architecture. Our model is pretrained on a large, unlabeled dataset of CT and MRI scans, leveraging the xLSTM backbone to capture long-range spatial dependencies and complex anatomical structures in 3D medical images. By incorporating multi-modal pretraining, our approach ensures strong generalization across both CT and MRI modalities, mitigating modality-specific variations and improving segmentation accuracy in diverse clinical settings. The use of large-scale unlabeled data significantly reduces the dependency on manual annotations, enabling robust performance even with limited labeled data. We further introduce an xLSTM-UNet-based architecture for downstream whole-heart segmentation tasks, demonstrating its effectiveness on few-label CT and MRI datasets. Our results validate the robustness and adaptability of the proposed model, highlighting its potential for advancing automated whole-heart segmentation in medical imaging.
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