Abstract：
BACKGROUND: Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brain malformations.
OBJECTIVE: The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational age prediction.
METHODS: In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age prediction performance.
RESULTS: Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an R 2 $R^2$ of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestational stage.
CONCLUSIONS: These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.
OBJECTIVE: The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational age prediction.
METHODS: In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age prediction performance.
RESULTS: Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an R 2 $R^2$ of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestational stage.
CONCLUSIONS: These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.
摘要：
背景：基于胎儿脑磁共振成像（MRI）的胎龄预测已被广泛用于表征正常胎儿脑发育和诊断先天性脑畸形。
目的：胎儿位置和外部干扰的不确定性导致胎儿大脑的位置和方向可变。此外,孕妇通常在胎儿异常扫描周专注于接受MRI扫描，导致胎儿脑MRI数据的不平衡分布。上述问题对基于深度学习的胎脑MRI胎龄预测提出了很大的挑战。
方法：在本研究中，提出了一种金字塔挤压注意力（PSA）引导的动态特征融合CNN（PDFF-CNN），用于根据不平衡数据集上的胎儿脑MRI图像来可靠地预测胎龄。PDFF-CNN包含四个组件：转换模块，特征提取模块,动态特征融合模块,和平衡均方误差(MSE)损失。通过使用PSA来使用变换和特征提取模块来学习并行权重共享暹罗网络中的多尺度和多方向特征表示。动态特征融合模块自动学习在特征提取模块中生成的特征向量的权重，以动态融合多尺度和多方向的脑沟和回旋特征。考虑到不平衡数据集的事实，平衡MSE损失用于减轻不平衡数据分布对胎龄预测性能的负面影响。
结果：对来自157名受试者的1327个常规临床T2加权MRI图像的不平衡胎儿脑MRI数据集进行评估，PDFF-CNN实现了有希望的胎龄预测性能，总体平均绝对误差为0.848周，R2为0.904。此外，得出了PDFF-CNN的注意力激活图，揭示了在每个妊娠阶段有助于预测孕龄的区域特征。
结论：这些结果表明，拟议的PDFF-CNN可能在指导治疗干预和分娩计划方面具有广泛的临床适用性。这可能有助于产前诊断。
目的：胎儿位置和外部干扰的不确定性导致胎儿大脑的位置和方向可变。此外,孕妇通常在胎儿异常扫描周专注于接受MRI扫描，导致胎儿脑MRI数据的不平衡分布。上述问题对基于深度学习的胎脑MRI胎龄预测提出了很大的挑战。
方法：在本研究中，提出了一种金字塔挤压注意力（PSA）引导的动态特征融合CNN（PDFF-CNN），用于根据不平衡数据集上的胎儿脑MRI图像来可靠地预测胎龄。PDFF-CNN包含四个组件：转换模块，特征提取模块,动态特征融合模块,和平衡均方误差(MSE)损失。通过使用PSA来使用变换和特征提取模块来学习并行权重共享暹罗网络中的多尺度和多方向特征表示。动态特征融合模块自动学习在特征提取模块中生成的特征向量的权重，以动态融合多尺度和多方向的脑沟和回旋特征。考虑到不平衡数据集的事实，平衡MSE损失用于减轻不平衡数据分布对胎龄预测性能的负面影响。
结果：对来自157名受试者的1327个常规临床T2加权MRI图像的不平衡胎儿脑MRI数据集进行评估，PDFF-CNN实现了有希望的胎龄预测性能，总体平均绝对误差为0.848周，R2为0.904。此外，得出了PDFF-CNN的注意力激活图，揭示了在每个妊娠阶段有助于预测孕龄的区域特征。
结论：这些结果表明，拟议的PDFF-CNN可能在指导治疗干预和分娩计划方面具有广泛的临床适用性。这可能有助于产前诊断。
