SEGMENTATION AND UNCERTAINTY MEASURES OF CARDIAC SUBSTRATES WITHIN OPTICAL COHERENCE TOMOGRAPHY IMAGES VIA CONVOLUTIONAL NEURAL NETWORKS

Segmentation of human cardiac tissue has a great potential to provide critical clinical guidance for Radiofrequency Ablation (RFA). Uncertainty in cardiac tissue segmentation is high because of the ambiguity of the subtle boundary and intra-/inter-physician variations. In this paper, we proposed a deep learning framework for Optical Coherence Tomography (OCT) cardiac segmentation with uncertainty measurement. Our proposed method employs additional dropout layers to assess the uncertainty of pixel-wise label prediction. In addition, we improve the segmentation performance by using focal loss to put more weights on mis-classified examples. Experimental results show that our method achieves high accuracy on pixel-wise label prediction. The feasibility of our method for uncertainty measurement is also demonstrated with excellent correspondence between uncertain regions within OCT images and heterogeneous regions within corresponding histology images.