Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of cardiac tissue substrates, the treatment of complex arrhythmias such as atrial fibrillation and ventricular tachycardia can be further optimized by detecting arrhythmia substrates to target for treatment (i.e., adipose) and identifying critical structures to avoid. Optical coherence tomography (OCT) is a real-time imaging modality that aids in addressing this need. Existing approaches for cardiac image analysis mainly rely on fully supervised learning techniques, which suffer from the drawback of workload on labor-intensive annotation process of pixel-wise labeling. To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates. In particular, we integrate class activation mapping with superpixel segmentation to solve the sparse tissue seed challenge raised in cardiac tissue segmentation. Our study bridges the gap between the demand on automatic tissue analysis and the lack of high-quality pixel-wise annotations. To the best of our knowledge, this is the first study that attempts to address cardiac tissue segmentation on OCT images via weakly supervised learning techniques. Within an in-vitro human cardiac OCT dataset, we demonstrate that our weakly supervised approach on image-level annotations achieves comparable performance as fully supervised methods trained on pixel-wise annotations.
翻译:自动识别心脏异常背后的结构性子质,可能会为干预程序提供实时指导。由于对心脏组织基质的了解,对复杂的心律不全的处理可以进一步优化,例如,通过检测心律不全的子质子质到治疗目标(即,脂肪)和确定关键结构以避免。光学一致性成像(OCT)是一种实时成像模式,有助于满足这一需求。现有的心脏成像分析方法主要依赖完全受监督的学习技术,这些技术由于劳动密集型的分解过程(例如,心脏组织基质标签的分解)的工作量的减少而受到影响。为了减少对像素贴标签的必要性,我们开发了一个两阶段的深度学习框架,利用人类心脏分质的OCT图像的图像级别说明进行心律分解。特别是,我们将级活动图解方法与超像素分解相结合,以解决心脏组织分解过程中产生的稀组织种子挑战。我们的研究在对精密的分解水平上,从劳动密集型分解过程中缩小了对劳动密集型分解过程的需求。我们关于通过自动组织分层分析进行最精确的奥分析的学习,这是我们内部分析的最佳分解方法的学习。