用于胎儿脑室体积测量与异常筛查的跨模态超声-MRI学习
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
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中文总结 AI 辅助
本研究提出跨模态学习框架VIFBA，基于超声视频实现胎儿脑室体积预测、VM严重程度分类及非VM异常筛查，性能优于基线与现有模型，为产前脑筛查提供实用方案。
中文摘要 AI 辅助
胎儿脑超声对脑室扩大（VM）的评估主要依赖于测量标准平面上的侧脑室房宽度，该方法依赖操作者经验，可能无法完全反映整体脑室扩张情况。胎儿脑MRI能提供更可靠的体积信息，但成本较高，常规使用可及性较低。为解决这些局限，我们提出VIFBA，一种基于超声视频的胎儿脑评估框架，可预测MRI衍生的侧脑室体积、对VM严重程度进行分类，并识别潜在非VM胎儿脑异常。我们的贡献有三：其一，引入受联合嵌入预测架构（JEPA）启发的管状隐变量预测目标，利用超声视频中的时空一致性增强表征学习；其二，开发对比跨模态对齐策略，训练时将MRI的结构信息迁移至超声，推理时仅需超声；其三，为VIFBA添加无需训练的视觉语言模型与检索增强模块，以验证不确定预测并识别潜在非VM胎儿脑异常。我们在包含857例配对胎儿脑超声与MRI检查的大型数据集（3196个视频）上验证了VIFBA。在保留的测试数据上，VIFBA在脑室体积回归任务中达到平均绝对误差（MAE）0.5909 mL、皮尔逊相关系数0.9907，VM严重程度分类准确率0.9400，多异常分类F1值0.7764，显著优于单任务基线、基于视频的强竞争对手及当前最优基础模型。通过仅用常规超声实现MRI辅助的体积评估，VIFBA为精准且低成本的产前脑筛查提供了实用且可能广泛部署的路径。
英文摘要
Assessment of ventriculomegaly (VM) on fetal brain ultrasound relies primarily on measuring lateral ventricular atrial width on standard planes, which is operator-dependent and may not fully reflect the overall ventricular enlargement. Fetal brain MRI provides more reliable volumetric information but is costly and less accessible for routine use. To address these limitations, we propose VIFBA, an ultrasound video-based framework for fetal brain assessment that predicts MRI-derived lateral ventricular volume, classifies VM severity, and identifies potential non-VM fetal brain abnormalities. Our contribution is three-fold. First, we introduce a joint-embedding predictive architecture (JEPA)-inspired tube latent prediction objective that leverages spatio-temporal coherence in ultrasound videos to enhance representation learning. Second, we develop a contrastive cross-modal alignment strategy that transfers structural information from MRI to ultrasound during training, while requiring ultrasound alone at inference. Third, we augment VIFBA with a training-free vision-language model and retrieval augmentation to verify uncertain predictions and identify potential non-VM fetal brain abnormalities. We validated VIFBA on a large dataset comprising 857 cases (3,196 videos) with paired fetal brain ultrasound and MRI examinations. On held-out test data, VIFBA achieved an MAE of 0.5909 mL and Pearson correlation coefficient of 0.9907 for ventricular volume regression, 0.9400 accuracy for VM severity classification, and an F1 score of 0.7764 for multi-abnormality classification, substantially outperforming single-task baselines, video-based strong competitors, and state-of-the-art foundation models. By enabling MRI-informed volumetric assessment from routine ultrasound alone, VIFBA offers a practical and potentially broadly deployable pathway toward accurate and affordable prenatal brain screening.
发表机构
- Harvard Medical School（哈佛医学院）
- Shenzhen University（深圳大学）
- Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）
- School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）
- School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
机构由 AI 辅助整理，请以论文原文为准。
