SV-Cine：基于生成式数据增强的单心室生理学诊断条件分割
SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation
- David Geffen School of Medicine at UCLA（加州大学洛杉矶分校大卫·格芬医学院）
- VA Greater Los Angeles（大洛杉矶退伍军人事务部医疗中心）
- University of California, Los Angeles（加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对单心室生理学罕见且形态多样的心脏MRI分割难题，提出基于生成式数据增强的诊断条件分割框架SV-Cine，通过合成数据扩充和特征级线性调制整合诊断信息，在右心室分割上超越基线0.39 Dice点。
AI中文摘要：
单心室生理学（SVP）是先天性心脏病的一种罕见亚型，其特征是存在单一功能性心室，并伴有非典型解剖结构，这对传统图像分割方法构成了挑战。临床数据的稀缺性和SVP亚型间的形态多样性使得开发稳健的分割方法尤为困难。为解决这些局限性，我们提出了一种针对SVP定制的心脏MRI分割框架，专注于心室腔和心肌的分割。首先，我们引入了一个数据增强流程，利用SDF4CHD生成合成3D心脏网格，并通过生成式建模生成相应的合成心脏MRI。其次，我们提出了SV-Cine，这是基础模型CineMA的一种诊断条件适应版本，通过特征级线性调制层整合患者层面的诊断信息，从而在分割过程中实现诊断感知的特征适应。我们在一个包含不同SVP亚型的内部队列上评估了该框架。SV-Cine在左心室分割上取得了中位Dice分数0.89（IQR：0.80--0.91），在右心室分割上取得了0.72（IQR：0.54--0.84），在右心室分割上比最强基线nnU-Net高出0.39个Dice点。对于主心室，其中位射血分数误差为5.55个百分点（IQR：3.41--7.69）。与内部队列相比，外部队列的左心室和心肌分割性能较低；而右心室的Dice分数在两个队列中相当。我们的研究结果表明，通过利用诊断先验，并借助预训练期间从大规模MRI数据集中学到的解剖知识，预训练基础模型可以适应高度专业化的下游任务。
英文摘要：
Single Ventricle Physiology (SVP) is a rare subtype of congenital heart disease characterized by the presence of a single functional cardiac ventricle with atypical anatomic configurations that challenge conventional image segmentation approaches. The scarcity of clinical data and the morphological diversity across SVP subtypes make the development of robust segmentation methods particularly difficult. To address these limitations, we propose a cardiac MRI segmentation framework focused on ventricular chambers and myocardium segmentation tailored for SVP. First, we introduce a data augmentation pipeline that generates synthetic 3D cardiac meshes using SDF4CHD and corresponding synthetic cardiac MRI through generative modeling. Second, we introduce SV-Cine, a diagnosis-conditioned adaptation of the foundation model CineMA that incorporates patient-level diagnostic information through Feature-wise Linear Modulation layers, enabling diagnosis-aware feature adaptation during segmentation. We evaluated the framework on an internal cohort with varying SVP subtypes. SV-Cine achieved median Dice scores of 0.89 (IQR: 0.80--0.91) for the left ventricle and 0.72 (IQR: 0.54--0.84) for the right ventricle, outperforming the strongest baseline, nnU-Net, by 0.39 Dice points on right ventricle segmentation. It also yields a median ejection fraction error of 5.55 percentage points (IQR: 3.41--7.69) for the dominant ventricle. Compared with the internal cohort, LV and myocardium segmentation performance was lower for the external cohort; whereas RV Dice scores were comparable for both cohorts. Our findings suggest that a pretrained foundation model can be adapted for highly specialized downstream tasks through usage of diagnosis priors while leveraging anatomic knowledge learned from large-scale MRI datasets during pretraining.
