Automated segmentation of cardiac structures in magnetic resonance imaging via deep convolutional neural networks

该项目的总体目标是设计一种全自动、高效的算法，用于在心脏CINE-MRI成像数据中发现几种心脏结构，例如左心室和右心室腔。计算这些结构转化为心脏形态和运动的综合测量，这在心脏病诊断中具有很高的临床意义。重点将放在基于卷积神经网络的最先进的深度学习方法上。具体的子目标包括：(1)研究一种新的半监督学习损失函数，该函数嵌入了考虑心脏解剖结构的几何先验。这些先验的目的是考虑在心脏图像分析的背景下，专家注释的训练数据的有限大小；(2)设计、实现和测试一个考虑心脏结构特定背景的卷积神经网络架构；(3)通过将结果与心脏病专家的基础事实注释进行比较来评估算法。这个ENGAGE研究项目将使lecorstem的算法与医学图像分析深度学习的前沿发展保持一致，并开发最先进的测试示例，从而有可能增强其产品组合和在快速增长的市场中的竞争力。通过尖端算法增强Corstem的产品组合，将有助于加拿大作为领先的人工智能和医疗成像技术生产商的地位。

The overall objective of this project is to design a fully automated and efficient algorithm for finding severalheart structures in cardiac CINE-MRI imaging data, e.g., the cavities of the left and right ventricles. Computingsuch structures translates into comprehensive measures of heart morphology and motion, which are of highclinical interest in cardiac disease diagnosis. The focus will be on state-of-the-art deep learning approachesbased on convolutional neural networks. Specific sub-objectives include: (1) investigating a novelsemi-supervised learning loss function, which embeds geometric priors accounting for the anatomy of theheart. The purpose of such priors is to account for the limited size of expert-annotated training data in thecontext of cardiac image analysis; (2) designing, implementing and testing a convolutional neural networkarchitecture that accounts for the specific context of cardiac structures; and (3) evaluating the algorithm bycomparing the results to ground-truth annotations by cardiologists. This ENGAGE research project will enableCorstem's algorithms to align with cutting-edge developments in deep learning for medical image analysis, andto develop stat-of-the-art test examples, with the potential of enhancing its product portfolio andcompetitiveness in a fast growing market. Enhancing Corstem's product portfolio with cutting-edge algorithmswill contribute to Canada's stature as a leading AI and medical imaging technology producer.