Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.

准确的心腔 3D 建模对于心脏容积和功能的临床评估（包括结构和运动分析）至关重要。此外，为了研究大量人群中心脏形态与其他患者信息之间的相关性，有必要自动生成人群中每个受试者的心脏网格模型。在这项研究中，我们介绍了 MCSI-Net（多线索形状推理网络），我们在其中将统计形状模型嵌入卷积神经网络中，并利用队列中的表型和人口统计信息来推断所有四个心脏的特定主题重建3D 腔室。通过这种方式，我们利用网络的能力来了解电影心脏磁共振 (CMR) 图像中心腔的外观，并生成合理的 3D 心脏形状，通过使用形状先验约束预测，以形状变化的统计模式的形式从人口的子集中先验学习。这反过来又使网络能够推广到整个人口的样本。据我们所知，这是第一项使用这种方法生成患者特定心脏形状的工作。MCSI-Net 能够使用可用图像数据的一小部分（约 23% 至 46%）生成准确的 3D 形状，这对社区非常重要，因为它支持加速 CMR 扫描采集。来自英国生物银行的心脏 MR 图像用于训练和验证所提出的方法。我们还展示了在 50 个时间范围内分析英国生物银行 40,000 名受试者的结果，总图像量 200 万。在存在切片间运动的情况下，我们的模型可以生成比手动注释更全局一致的心脏形状，并且与心室和心房的心脏结构和功能的参考范围非常一致。