Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in this study is to aid 3D fetal vessel topology visualisation in aortic arch anomalies, a group which encompasses a range of conditions with significant anatomical heterogeneity. We present a multi-task framework for automated multi-class fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. Our training data consists of binary manual segmentation masks of the cardiac vessels' region in individual subjects and fully-labelled anomaly-specific population atlases. Our framework combines deep learning label propagation using VoxelMorph with 3D Attention U-Net segmentation and DenseNet121 anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. We incorporate an anomaly classifier into our segmentation pipeline, delivering a multi-task framework with the primary motivation of correcting topological inaccuracies of the segmentation. The hypothesis is that the multi-task approach will encourage the segmenter network to learn anomaly-specific features. As a secondary motivation, an automated diagnosis tool may have the potential to enhance diagnostic confidence in a decision support setting. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels. Our classifier outperforms a classifier trained exclusively on T2w volume images, with an average balanced accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.

翻译：先天性心脏病（CHD）是一组胎儿期即存在的心脏畸形，是全球范围内最常见的出生缺陷类别。本研究旨在辅助三维胎儿血管拓扑结构可视化，针对主动脉弓异常——该类涵盖一系列具有显著解剖异质性的病变。我们提出一种多任务框架，用于从三维黑血T2加权MRI中实现自动化多类别胎儿血管分割及异常分类。训练数据包括个体受试者心脏血管区域的二值人工分割掩膜以及完全标注的异常特异性群体图谱。该框架结合基于VoxelMorph的深度学习标签传播、三维注意力U-Net分割及DenseNet121异常分类。我们针对11条心脏血管及三种不同主动脉弓异常（包括双主动脉弓、右位主动脉弓及可疑主动脉缩窄）进行建模。通过将异常分类器整合至分割流程，构建多任务框架，其主要动机在于纠正分割中的拓扑不准确性。假设多任务方法能促使分割网络学习异常特异性特征。次要动机在于，自动化诊断工具在决策支持场景中可能提升诊断置信度。实验结果表明，所提出的训练策略显著优于标签传播及单纯基于传播标签训练的网络。经联合训练后，分类器平均平衡精度达0.99（标准差0.01），优于单纯基于T2加权容积图像训练的分类器。引入分类器使所有正确分类的双主动脉弓受试者的解剖与拓扑精度均得到提升。