Xing Jiarui, Wang Shuo, Bilchick Kenneth C, Patel Amit R, Zhang Miaomiao
Department of Electrical and Computer Engineering, University of Virginia, USA.
School of Medicine, University of Virginia Health System, USA.
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023. doi: 10.1109/isbi53787.2023.10230541. Epub 2023 Sep 1.
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect. This paper presents a novel joint deep learning (JDL) framework that improves such tasks by utilizing simultaneously learned myocardium segmentations to eliminate negative effects from non-region-of-interest areas. In contrast to previous approaches treating scar detection and myocardium segmentation as separate or parallel tasks, our proposed method introduces a message passing module where the information of myocardium segmentation is directly passed to guide scar detectors. This newly designed network will efficiently exploit joint information from the two related tasks and use all available sources of myocardium segmentation to benefit scar identification. We demonstrate the effectiveness of JDL on LGE-CMR images for automated left ventricular (LV) scar detection, with great potential to improve risk prediction in patients with both ischemic and non-ischemic heart disease and to improve response rates to cardiac resynchronization therapy (CRT) for heart failure patients. Experimental results show that our proposed approach outperforms multiple state-of-the-art methods, including commonly used two-step segmentation-classification networks, and multitask learning schemes where subtasks are indirectly interacted.
从延迟钆增强心脏磁共振图像（LGE-CMR）中自动识别心肌瘢痕受到图像噪声和伪影的限制，如与运动和部分容积效应相关的伪影。本文提出了一种新颖的联合深度学习（JDL）框架，该框架通过利用同时学习的心肌分割来消除非感兴趣区域的负面影响，从而改进此类任务。与以往将瘢痕检测和心肌分割视为单独或并行任务的方法不同，我们提出的方法引入了一个消息传递模块，在该模块中，心肌分割的信息被直接传递以指导瘢痕检测器。这个新设计的网络将有效地利用来自这两个相关任务的联合信息，并使用所有可用的心肌分割源来促进瘢痕识别。我们证明了JDL在LGE-CMR图像上用于自动左心室（LV）瘢痕检测的有效性，具有极大的潜力来改善缺血性和非缺血性心脏病患者的风险预测，并提高心力衰竭患者心脏再同步治疗（CRT）的反应率。实验结果表明，我们提出的方法优于多种先进方法，包括常用的两步分割分类网络以及子任务间接交互的多任务学习方案。
