用PCA和多方法融合实现早期胚胎3D超声图像的高精度自动对齐。
Robust Alignment of the Human Embryo in 3D Ultrasound using PCA and an Ensemble of Heuristic, Atlas-based and Learning-based Classifiers Evaluated on the Rotterdam Periconceptional Cohort
- 基于PCA提取胚胎主轴，生成四个候选朝向。
- 三种方法分别达95.8%~98.4%准确率，集成后达98.5%。
- 适合需要批量处理的产前影像研究与临床分析。
标准化三维超声图像中胚胎的对齐有助于产前生长监测，便于标准切面识别、提升解剖标志可视化并凸显不同扫描间的差异。本文提出一种自动化对齐方法：给定胚胎分割掩码后，使用主成分分析（PCA）提取其主轴，生成四个候选朝向；通过三种策略选择标准朝向——基于皮尔逊相关性的形状启发式、基于归一化互相关与图谱的图像匹配、以及随机森林分类器。在来自鹿特丹围孕期队列的1043例妊娠共2166张纵向3D超声图像上测试，7+0至12+6周孕期数据中，PCA在99.0%图像中正确提取主轴；三种选择策略的准确率分别为97.4%、95.8%和98.4%，多数投票后整体准确率达98.5%。该高精度流程可实现第一孕期胚胎对齐的一致性，支持临床与科研中的规模化分析。代码已公开于：https://gitlab.com/radiology/prenatal-image-analysis/pca-3d-alignment。
原文摘要 · Abstract (English)
Standardized alignment of the embryo in three-dimensional (3D) ultrasound images aids prenatal growth monitoring by facilitating standard plane detection, improving visualization of landmarks and accentuating differences between different scans. In this work, we propose an automated method for standardizing this alignment. Given a segmentation mask of the embryo, Principal Component Analysis (PCA) is applied to the mask extracting the embryo's principal axes, from which four candidate orientations are derived. The candidate in standard orientation is selected using one of three strategies: a heuristic based on Pearson's correlation assessing shape, image matching to an atlas through normalized cross-correlation, and a Random Forest classifier. We tested our method on 2166 images longitudinally acquired 3D ultrasound scans from 1043 pregnancies from the Rotterdam Periconceptional Cohort, ranging from 7+0 to 12+6 weeks of gestational age. In 99.0% of images, PCA correctly extracted the principal axes of the embryo. The correct candidate was selected by the Pearson Heuristic, Atlas-based and Random Forest in 97.4%, 95.8%, and 98.4% of images, respectively. A Majority Vote of these selection methods resulted in an accuracy of 98.5%. The high accuracy of this pipeline enables consistent embryonic alignment in the first trimester, enabling scalable analysis in both clinical and research settings. The code is publicly available at: https://gitlab.com/radiology/prenatal-image-analysis/pca-3d-alignment.
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