迈向可靠且可重复的胎儿脑生物测量：一种基于MRI的深度学习方法
Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI
AI总结：
本文提出一种基于深度学习的四步骤自动化流程，从胎儿MRI中可靠可重复地进行脑生物测量，在两个含150例数据的公开数据集上验证，精度优于或相当现有方法，可支持临床应用。
AI中文摘要：
胎儿脑生物测量对于定量评估脑发育至关重要，可用于支持胎龄估算、发育监测及异常检测。临床实践中，该测量多为手动操作，耗时且易产生变异。虽已有自动化方法被提出，但可靠且可重复的方法仍有限，尤其是能提供解剖学可解释性标志点定位的方法。本文提出一种完全自动化的基于深度学习的框架，用于从3D超分辨率重建的胎儿脑MRI中进行可靠且可重复的脑生物测量。所提出的四步骤流程通过联合估算线性测量值及其对应的解剖学标志点来推导生物测量参数。训练3D卷积神经网络以从脑分割标签图中回归标志点坐标，随后进行测量特定的几何优化以细化标志点位置并计算测量值。该流程在两个公开可用的胎儿MRI数据集上进行评估，共包含150个体积（胎龄范围为20-37周），这些数据来自不同扫描仪及采集方案，评估了不同采集设置下的5项关键生物测量指标，并通过定量指标和视觉评估对测量精度及标志点定位进行了全面评估。与唯一可用的自动化流程相比，所提方法在大多数测量指标上达到了相当或更高的精度。综上，本文提出了一种用于可靠生物测量估算的简单流程，其具备效率、可解释性及可扩展性，支持集成至临床工作流程中。
英文摘要：
Fetal brain biometry is essential for quantitative assessment of brain development, supporting gestational age estimation, developmental monitoring, and detection of abnormalities. In clinical practice, measurements are manually performed, making them time-consuming and prone to variability. While automated approaches have been proposed, reproducible methods remain limited, particularly those providing anatomically interpretable landmark localization. We present a fully automated deep learning-based framework for reliable and reproducible brain biometry from 3D super-resolution-reconstructed fetal brain MRI. The proposed four-step pipeline derives biometric parameters by jointly estimating linear measurements and their corresponding anatomical landmarks. A 3D convolutional neural network is trained to regress landmark coordinates from brain segmentation label maps, followed by measurement-specific geometric optimization to refine landmark positions and compute measurements. The pipeline is evaluated on two publicly available fetal MRI datasets comprising 150 volumes (gestational age range: 20-37 weeks) acquired across different scanners and protocols, assessing five key biometric measurements across varying acquisition settings and providing a comprehensive evaluation of both measurement accuracy and landmark localization using quantitative metrics and visual assessment. Compared with the only available automated pipeline, the proposed method achieves comparable or improved accuracy for most measurements. In conclusion, we introduce a straightforward pipeline for reliable biometry estimations, with efficiency, interpretability and scalability that support integration into clinical workflows.
