Deep Learning-based Framework for Segmentation and Motion Tracking of Left Ventricle in 3D Echocardiography

基于深度学习的 3D 超声心动图左心室分割和运动跟踪框架

• 批准号: 10563111
• 负责人: Shawn Ahn
• 金额: $ 3.16万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10563111
• 关键词: 3-Dimensional; Acute; Acute myocardial infarction; Address; Algorithms; Appearance; Area; Assessment tool; Attention; Cardiac; Cause of Death; Clinical; Communities; Congestive Heart Failure; Coronary Arteriosclerosis; Crystallization; Data; Data Set; Detection; Diagnosis; Disadvantaged; Echocardiography; Effectiveness; Event; Exercise; Fellowship; Functional disorder; Goals; Gold; Heart; High Resolution Computed Tomography; Image; Image Analysis; Imaging Techniques; Implant; Infarction; Injury; Interobserver Variability; Ischemia; Label; Lead; Learning; Left Ventricular Remodeling; Left ventricular structure; Manuals; Maps; Measurement; Mechanics; Medical Imaging; Methods; Modeling; Morbidity - disease rate; Motion; Myocardial; Myocardial Infarction; Myocardium; Nature; Neural Network Simulation; Noise; Organ; Patients; Pattern; Performance; Pharmacology; Process; Public Health; Radial; Research; Rest; Shapes; Signal Transduction; Stress; Stress Echocardiography; Surface; Therapeutic; Three-Dimensional Echocardiography; Time; Training; Two-Dimensional Echocardiography; United States; Visual; Work; X-Ray Computed Tomography; base; canine model; clinical practice; convolutional neural network; cost effective; deep learning; deep learning algorithm; feature extraction; heart function; heart imaging; imaging modality; improved; mortality; myocardial injury; neural network; neural network architecture; novel; outcome prediction; radio frequency; screening; segmentation algorithm; spatiotemporal; tool; two-dimensional; vector

PROJECT SUMMARY/ABSTRACT Coronary artery disease remains the leading cause of death around the world. Acute myocardial infarction (MI) causes regional dysfunction which places remote areas of the heart at a mechanical disadvantage resulting in long term adverse left ventricular (LV) remodeling and complicated congestive heart failure (CHF). Stress echocardiography is currently the clinically established, cost-effective 2D imaging technique for detecting and characterizing myocardial injury by imaging the left ventricle at rest and after either exercise or pharmacologically-induced stress to reveal ischemia and/or infarct. However, the inherent limitations of a 2D echocardiography make it difficult to characterize the whole 3D volume of ischemic/infarct zone, and the qualitative assessment of wall-motion abnormality to characterize myocardial deformation leads to variability among experts. Although 3D echocardiography has potential to address the limitations of 2D imaging, it is not widely accepted in standard clinical use due to the low signal-to-noise ratio (SNR). With the recent advancements in deep learning algorithms, many segmentation and registration tasks have achieved near expert level accuracy. Also, previous works have shown the utility of strain analysis as a way to quantify the degree of wall-motion abnormality in cardiac imaging modalities. Still, many of the current deep learning frameworks focus largely on intensity-based features which are still difficult to train on 3D echocardiography datasets, which in turn leads to poor strain analysis. Thus, in this fellowship, I propose to develop novel data-driven neural network models specifically tailored to 3D echocardiography to improve segmentation and motion tracking of left ventricle in order to achieve full 3D cardiac strain analysis. My first aim is to develop a multi-frame attention-based neural network to exploit the spatiotemporal features of the echocardiography dataset to improve 3D segmentation of left ventricle. This method will take advantage of the inter-frame spatiotemporal features to augment the relevant feature extractions for segmentation. My second aim is to develop a registration neural network in 3D echocardiography by combining intensity-based features and surface-curvature bending energy to improve the motion tracking of left ventricle. This neural network will build upon the accurate segmentations from the first aim to include unique curvature energy features at the boundaries to enhance tracking accuracy at all areas of the myocardium. The improved motion tracking will be used to calculate strain for detection of full 3D ischemic/infarct zones. In summary, this research will provide an objective, quantitative tools for characterizing wall-motion abnormality with strain analysis in 3D echocardiography.

项目摘要/摘要 冠状动脉疾病仍然是世界各地死亡的主要原因。急性心肌梗塞（MI） 导致区域功能障碍，使心脏的偏远区域处于机械劣势，导致 长期不良左心室（LV）重塑和复杂的充血性心力衰竭（CHF）。压力 超声心动图目前是用于检测和的临床成本有效的2D成像技术 通过在休息和锻炼或 药理学引起的应力显示缺血和/或梗死。但是，2D的固有局限性 超声心动图使很难表征整个3D缺血/梗塞区的3D体积，并且 壁性异常的定性评估以表征心肌变形导致可变性 在专家中。尽管3D超声心动图具有解决2D成像的局限性的潜力，但不是 由于低信噪比（SNR），在标准临床使用中广泛接受。随着最近的进步 在深度学习算法中，许多细分和注册任务已接近专家级别的准确性。 此外，以前的工作已经显示了应变分析的效用，以量化壁感觉的程度 心脏成像方式异常。尽管如此，许多当前的深度学习框架主要集中在 基于强度的功能仍然很难在3D超声心动图数据集上进行训练，这又导致 劳力分析差。因此，在这个奖学金中，我建议开发新型数据驱动的神经网络模型 专门针对3D超声心动图定制，以改善左心室的分割和运动跟踪 实现完整的3D心脏应变分析。我的第一个目的是开发基于注意力的神经 用于利用超声心动图数据集的时空特征以改进3D 左心室的细分。此方法将利用框架间时空特征 增加相关特征提取以进行分割。我的第二个目的是开发注册神经 通过结合基于强度的特征和表面曲线弯曲的3D超声心动图中的网络 能够改善左心室运动跟踪的能量。这个神经网络将基于准确的 从第一个目的的分割，以在边界上包含独特的曲率能量特征以增强 跟踪心肌的所有区域的准确性。改进的运动跟踪将用于计算应变 用于检测完整的3D缺血/梗塞区。总而言之，这项研究将提供一个客观的定量 通过3D超声心动图中的应变分析来表征壁感觉异常的工具。