Automated Processing and Analysis of the Human Right Ventricle for the Detection of Pulmonary Hypertension

心血管健康一直是并将继续是世界卫生保健领域最重要的研究课题之一。特别是高血压，多年来一直是人们关注的主要问题，然而，由于持续压力过载导致的心功能变化仍然没有得到很好的理解。例如，肺动脉高压（PH）是一种致命的疾病，众所周知，它会显著改变心脏的外观和功能，尤其是右心室。然而，没有明确的定量指标与心脏的这些变化有关，医生可以准确预测PH患者的预后。先前的研究表明，右心室的形状和PH 2的进展之间存在关系。更具体地说，先前的工作依赖于通过谐波映射到球体来描述右心室心内膜表面（RVES）的形状，并通过PCA/POD 1或球面谐波2等直接方法降低维数。然而，这些方法需要在训练有素的心脏病专家的监督下进行手动校准和特征识别，这导致了大量的预处理费用和减少的数据集来训练分类器。这些预处理挑战是阻碍进一步研究RVES形状与ph状态之间联系的最大限制。提出的研究的主要目标是开发一种用于自动图像提取和处理的机器学习方法，以评估与ph状态相关的人类心脏形状和功能相关的模式。预计将探索几种途径来整合机器学习，以大幅提高分析心脏形状过程的效率和可靠性。潜在的探索领域包括拉普拉斯-贝尔特拉米表面映射（Laplace-Beltrami Surface Mapping）等技术，该技术允许自动检测极点和日期线特征，并且先前已被证明可以为大脑内部结构创建谐波图。另一种方法是通过使用旋转不变性特征4,5来避免手动对齐的需要。另外，对于更大的预处理数据集，更先进的神经网络技术，如自动编码器或深度信念网络，可以用于降维。通过卷积网络直接应用于调和映射球体的表面8或图像数据，或者通过将完全连接的网络应用于通过前面提到的方法检测到的特征9，也可以通过神经网络进行直接分类。参考文献1吴杰，等。理论物理。中华医学杂志，2012,32(5):481 - 481。第一版。方法>。生物医学。Eng。3 Shi， Y. et al. in Medical Image Computing and Computer- assisted Intervention (MICCAI) 2008 147-154（施普林格Berlin Heidelberg， 2008）4 Kazhdan， M. et al. in Proceedings of 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing 156-164 (2003)5 Skibbe， H. et al. in 2009 IEEE第十二届国际计算机视觉研讨会，ICCV研讨会2009 1863-1869 (2009)6 Baldi， P. 37-49 (2012)7 Hinton， G. E.等。[8]王晓东，王晓东，王晓东，等。基于神经网络的人工智能研究[j] .计算机工程学报，2016,33(5):557 - 557（2006）。图36,1-10 (2017)9 LeCun， Y. et al. in 9-50（施普林格，Berlin, Heidelberg, 1998）

Cardiovascular health has been and continues to be one of the most important research topics in worldwide healthcare. Hypertension in particular has been a major concern for many years, and yet, the changes in heart function due to sustained pressure overload are still not well understood. For example, pulmonary hypertension (PH) is a deadly disease that is well known to considerably change the appearance and function of the heart, especially the right ventricle 1. However, there are no clear quantitative metrics relating to these changes in the heart that are available to physicians to accurately predict PH patient outcomes.Previous research has shown the existence of a relationship between the shape of the right ventricle and the progression of PH 2. More specifically, prior work has relied upon describing the shape of the right ventricle endocardial surface (RVES) through harmonic mappings to the sphere, with dimensionality reduction through direct methods such as PCA/POD 1 or spherical harmonics 2. However, these methods required manual alignment and feature identification under supervision from a trained cardiologist, resulting in significant preprocessing expense and a reduced dataset to train classifiers on. These preprocessing challenges are the most significant limitation in preventing further investigation of this link between RVES shape and the state of PH. As such, the main objective of the proposed research is to develop a machine learning approach for automated image extraction and processing to evaluate patterns relating to the shape and function of the human heart related to the state of PH. Several avenues are expected to be explored for integrating machine learning to substantially improve the efficiency and reliability of the process to analyse heart shape. Potential areas to explore include techniques such as Laplace-Beltrami Surface Mapping, which allow for automated detection of pole and dateline features, and have previously been demonstrated to create harmonic maps for structures within the brain 3. Another approach would be to avoid the need for manual alignment through the use of rotationally invariant features 4,5. Alternatively, with a much larger set of preprocessed data, more advanced neural network techniques such as autoencoders 6 or deep belief networks 7 could be used for dimensionality reduction. Direct classification through neural networks is also possible through convolutional networks applied to the surface of the harmonically mapped sphere 8 or the image data directly, or by fully connected networks applied to features detected through previously mentioned methods 9. References1 Wu, J. et al. Phys. Med. Biol. 57, 7905 (2012)2 Wu, J. et al. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 4, 327-343 (2016)3 Shi, Y. et al. in Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2008 147-154 (Springer Berlin Heidelberg, 2008)4 Kazhdan, M. et al. in Proceedings of the 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing 156-164 (2003)5 Skibbe, H. et al. in 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 1863-1869 (2009)6 Baldi, P. 37-49 (2012)7 Hinton, G. E. et al. Neural Comput. 18, 1527-1554 (2006)8 Maron, H. et al. {ACM} Trans. Graph. 36, 1-10 (2017)9 LeCun, Y. et al. in 9-50 (Springer, Berlin, Heidelberg, 1998)