  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.
