使用 nnU-NetV2 自动分割胎儿四腔心 15 个关键解剖标签和心轴及心胸比测量值。

BACKGROUND: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. METHODS: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. RESULTS: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers. CONCLUSION: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

背景：准确分割胎儿四腔心切面图像中的关键解剖结构对于早期发现先天性心脏病至关重要。目前的产前筛查方法依赖于手动测量，既耗时又容易受到观察者间差异的影响。本研究开发了一种基于人工智能的模型，该模型使用最先进的 nnU-NetV2 架构，用于自动分割和测量胎儿四腔心切面图像中的关键解剖结构。

方法：一个包含 1083 高质量胎儿四腔心切面图像的数据集，使用 15 个关键解剖标签进行了注释，并分为训练/验证（867 个图像）和测试（216 个图像）集。使用 nnU-NetV2 架构的人工智能模型在标注图像上进行训练，并使用平均 Dice 系数（mDice）和平均交并比（mIoU）指标进行评估。将模型在自动计算心脏轴（CAx）和心胸比（CTR）方面的性能与不同经验水平的超声医师的测量结果进行了比较。

结果：基于人工智能的模型在分割关键解剖结构方面的平均 Dice 系数为 87.11%，平均交并比为 77.68%。该模型的自动 CAx 和 CTR 测量结果与经验丰富的超声医师的测量结果具有很强的一致性，相应的组内相关系数（ICC）分别为 0.83 和 0.81。Bland-Altman 分析进一步证实了模型与经验丰富的超声医师之间的高度一致性。

结论：我们开发了一种基于人工智能的模型，该模型使用 nnU-NetV2 架构用于准确分割和自动测量胎儿四腔心切面图像中的关键解剖结构。我们的模型在计算临床相关参数方面表现出了很高的分割准确性和与经验丰富的超声医师的高度一致性。这种方法有可能提高产前心脏筛查的效率和可靠性，最终有助于早期发现先天性心脏病。