In the past decades, medical imaging modalities including ultrasound,6–8 multidetector computed tomography,9,10 and magnetic resonance imaging have been used to characterize cardiac tissue compositions such as collagen region during myocardial infarction,6,9,10 adipose tissue,8,11 or organization of myofibers within myocardium.7,12 However, the abovementioned modalities suffer from either a low image resolution or a long data acquisition time. Optical coherence tomography (OCT) has been demonstrated to have the ability to image biological tissue at a fast rate with a high resolution () with a 2 mm imaging range13,14 in the axial direction. Previous research efforts demonstrated that OCT can image important features within the heart15 such as the purkinjie network,16 atrial ventricular nodes,17,18 sinoatrial nodes,19 and myofiber organization.20–23 Given that the wall thickness in the human atria ranges from 2 to 5 mm,24 OCT has the ability to visualize a large percentage of the human atrial wall. There is a great potential to classify tissue compositions within human atria via OCT imaging. However, manual interpretation of OCT images is time consuming and not applicable for analysis on large three-dimensional (3-D) volumetric datasets. Therefore, automated identification of tissue composition in human atria from OCT images is greatly needed.