面向MRI中肝硬化体积分割的协同深度学习模型
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
AI总结：
本文提出nnSynergyNet3D，通过融合连续与离散潜在空间增强三维特征交互建模，在肝硬化MRI分割中较nnUNet3D提升约2%，并在CT数据上展现出良好的跨模态泛化能力。
摘要：
肝硬化是全球主要致死原因之一，需要对感兴趣区域（ROI）进行精确分割，以实现有效的疾病监测和治疗规划。现有分割模型往往难以捕捉复杂的特征交互，并且在不同数据集上的泛化能力不足。为解决这些局限性，我们提出一种新颖的协同理论，利用互补的潜在空间来增强特征交互建模。我们提出的架构nnSynergyNet3D为三维体数据整合了连续和离散潜在空间，并具备自动配置训练功能。该方法能够同时捕捉细粒度和粗粒度特征，从而有效建模复杂的特征交互。我们在一个包含339名患者、628例高分辨率T1腹部MRI扫描的私有数据集上对nnSynergyNet3D进行了实证验证。我们的模型相较于基线nnUNet3D性能提升约2%。此外，在公开LiTS数据集上的健康肝脏CT扫描零样本测试表明，该模型具有优越的跨模态泛化能力。这些结果凸显了协同潜在空间模型在提高分割精度和鲁棒性方面的潜力，从而通过确保CT和MRI模态间的一致性来改进临床工作流程。
英文摘要：
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across diverse datasets. To address these limitations, we propose a novel synergistic theory that leverages complementary latent spaces for enhanced feature interaction modeling. Our proposed architecture, nnSynergyNet3D integrates continuous and discrete latent spaces for 3D volumes and features auto-configured training. This approach captures both fine-grained and coarse features, enabling effective modeling of intricate feature interactions. We empirically validated nnSynergyNet3D on a private dataset of 628 high-resolution T1 abdominal MRI scans from 339 patients. Our model outperformed the baseline nnUNet3D by approximately 2%. Additionally, zero-shot testing on healthy liver CT scans from the public LiTS dataset demonstrated superior cross-modal generalization capabilities. These results highlight the potential of synergistic latent space models to improve segmentation accuracy and robustness, thereby enhancing clinical workflows by ensuring consistency across CT and MRI modalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
