基于机器学习的肺部CT图像非刚性配准误差预测方法

配准误差评估通常由人工完成，耗时费力；常用的Dice测度只关注组织边缘的配准误差，难以评估组织内部配准结果。针对以上问题，提出一种基于机器学习的肺部CT图像非刚性配准误差预测方法(PREML)。该方法首先构建形变场统计特征、形变场物理保真度特征和图像相似性特征三类特征，然后通过池化方法扩充特征数量，最后使用随机森林回归方法预测非刚性配准误差，并且使用自适应随机扰动方法模拟肺部配准误差空间分布，进一步提升形变场统计特征的配准误差表征能力。在三个肺部CT图像数据集上进行训练与测试，其配准误差预测结果与金标准之间的平均绝对差异为1.245±2.500 mm,预测性能优于基线方法。结果表明，PREML方法具有预测精度高、鲁棒性强的特点，可提升配准算法在临床应用的有效性和安全性。

Error prediction for lung CT images nonrigid registration based on machine learning

The registration quality assessment is usually given to human experts, which is time-consuming. The commonly used Dice score only focuses on the error at the edge of the tissue, which is difficult to assess the registration result within the tissue. To address these issues, this paper proposed a method to predict registration errors based on machine learning(PREML) in lung CT images. This method firstly constructed three types of features, such as deformation field statistical features, deformation field physiologically realistic features and image similarity features, then expanded the number of features by pooling, and finally used random forest regression to predict non-rigid registration errors. Moreover, it used an adaptive random perturbation to simulate the spatial distribution of lung registration errors to further improve the capability of error characterization of statistical features. The proposed method achieved a mean absolute error of 1.245±2.500 mm from ground truth on lung CT image datasets, outperforming the baseline method. The results show that PREML method has the advantages of high accuracy and robustness, enhancing the safety and effectiveness of registration algorithms in clinical applications.