Kalapos András, Szabó Liliána, Dohy Zsófia, Kiss Máté, Merkely Béla, Gyires-Tóth Bálint, Vágó Hajnalka
Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.
Semmelweis University, Heart and Vascular Centre, Budapest, Hungary.
Front Cardiovasc Med. 2023 Jul 7;10:1147581. doi: 10.3389/fcvm.2023.1147581. eCollection 2023.
Structural and functional heart abnormalities can be examined non-invasively with cardiac magnetic resonance imaging (CMR). Thanks to the development of MR devices, diagnostic scans can capture more and more relevant information about possible heart diseases. T1 and T2 mapping are such novel technology, providing tissue specific information even without the administration of contrast material. Artificial intelligence solutions based on deep learning have demonstrated state-of-the-art results in many application areas, including medical imaging. More specifically, automated tools applied at cine sequences have revolutionized volumetric CMR reporting in the past five years. Applying deep learning models to T1 and T2 mapping images can similarly improve the efficiency of post-processing pipelines and consequently facilitate diagnostic processes.
In this paper, we introduce a deep learning model for myocardium segmentation trained on over 7,000 raw CMR images from 262 subjects of heterogeneous disease etiology. The data were labeled by three experts. As part of the evaluation, Dice score and Hausdorff distance among experts is calculated, and the expert consensus is compared with the model's predictions.
Our deep learning method achieves 86% mean Dice score, while contours provided by three experts on the same data show 90% mean Dice score. The method's accuracy is consistent across epicardial and endocardial contours, and on basal, midventricular slices, with only 5% lower results on apical slices, which are often challenging even for experts.
We trained and evaluated a deep learning based segmentation model on 262 heterogeneous CMR cases. Applying deep neural networks to T1 and T2 mapping could similarly improve diagnostic practices. Using the fine details of T1 and T2 mapping images and high-quality labels, the objective of this research is to approach human segmentation accuracy with deep learning.
心脏磁共振成像（CMR）可对心脏的结构和功能异常进行无创检查。得益于磁共振设备的发展，诊断扫描能够获取越来越多与可能的心脏病相关的信息。T1和T2映射就是这样的新技术，即使不使用造影剂也能提供组织特异性信息。基于深度学习的人工智能解决方案在包括医学成像在内的许多应用领域都取得了领先成果。更具体地说，在过去五年中，应用于电影序列的自动化工具彻底改变了容积CMR报告。将深度学习模型应用于T1和T2映射图像同样可以提高后处理流程的效率，从而促进诊断过程。
在本文中，我们介绍了一种用于心肌分割的深度学习模型，该模型在来自262名病因各异的受试者的7000多张原始CMR图像上进行训练。数据由三位专家标注。作为评估的一部分，计算专家之间的骰子系数和豪斯多夫距离，并将专家共识与模型预测进行比较。
我们的深度学习方法平均骰子系数达到86%，而三位专家对相同数据提供的轮廓平均骰子系数为90%。该方法在心外膜和心内膜轮廓以及基底、心室中部切片上的准确性是一致的，在心尖切片上的结果仅低5%，即使对专家来说心尖切片也往往具有挑战性。
我们在262个异质性CMR病例上训练并评估了一个基于深度学习的分割模型。将深度神经网络应用于T1和T2映射同样可以改善诊断实践。利用T1和T2映射图像的精细细节和高质量标签，本研究的目标是通过深度学习接近人类分割的准确性。
