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

用于检测肺动脉高压的人右心室自动处理和分析

• 批准号: 2115404
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2115404
• 关键词: Automated; Processing; Analysis; Human; Right

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)

心血管健康一直是并将继续是全球医疗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一个主要的问题，然而，由于持续的压力超负荷引起的心脏功能的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，特别是右心室1。然而，目前还没有与心脏这些变化相关的明确定量指标可供医生准确预测PH患者的结局。之前的研究表明，右心室形状与PH 2的进展之间存在关系。更具体地，现有工作依赖于通过到球体的谐波映射来描述右心室内表面（RVES）的形状，其中通过诸如PCA/POD 1或球谐函数2的直接方法进行降维。然而，这些方法需要在受过训练的心脏病专家的监督下进行手动对齐和特征识别，从而导致显著的预处理费用和减少的训练分类器的数据集。这些预处理挑战是阻止进一步研究RVES形状和PH状态之间的这种联系的最重要限制。拟议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的形状和功能相关的模式。探索整合机器学习，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Laplace-Beltrami Surface Mapping等技术，该技术允许自动检测极点和日界线特征，并且先前已被证明可以为大脑内的结构创建谐波图3。另一种方法是通过使用旋转不变特征4、5来避免手动对准的需要。或者，对于更大的预处理数据集，可以使用更先进的神经网络技术，例如自动编码器6或深度置信网络7来进行降维。通过直接应用于谐波映射的球体8的表面或图像数据的卷积网络，或者通过应用于通过前述方法9检测的特征的全连接网络，也可以通过神经网络进行直接分类。参考文献1 Wu，J.等人Phys.Med.Biol.57，7905（2012）2 Wu，J.等人Comput.方法生物力学。BioMed.工程成像维斯。4，327-343（2016）3 Shi，Y.等人在Medical Image Computing and Computer-Assisted Intervention（MICCAI）2008 147-154（Springer柏林海德堡，2008）4 Kazhdan，M.等人在Proceedings of the 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing 156-164（2003）5 Skibbe，H.等人在2009 IEEE第12届计算机视觉研讨会国际会议，ICCV研讨会2009 1863-1869（2009）6 Baldi，P. 37-49（2012）7欣顿，G. E.等人，Neural Comput. 18，1527-1554（2006）8 Maron，H.等.{ACM} Trans. Graph. 36，1-10（2017）9 LeCun，Y.等，9-50（Springer，柏林，海德堡，1998）