To evaluate the accuracy of the extracted results, we compared segmentations of 2-D cross-sectional images performed with our extraction algorithm and manually. For this study, manual segmentation was performed at three positions along the OFT: near the inlet, approximately in the middle, and near the outlet; at three different time points during the cardiac cycle: when the OFT walls are closed, half-closed, or opened (see Fig. 9, first to third rows). To compare results we used the areas enclosed by contours obtained by manual segmentation, ; contours obtained by automatic segmentation, ; and , which is the area of the intersection between the automatic and manually extracted regions. Two metrics, recall and precision, were used to evaluate the accuracy of the automatic segmentation. The recall, defined as , measures the ability of the automatic extracted result to match the target tissue; the precision, defined as , measures the ability of the automatic extracted result to contain only the target tissue. The average recall of the myocardium from the nine selected 2-D cross-sectional images was 84.7%, with a standard deviation of 3.3%, and its precision was 92.8%, with a standard deviation of 4.4%. The average recall of the lumen from the nine selected cross-sectional images was 91.9%, with a standard deviation of 3.3%, and its precision was 93.2%, with a standard deviation of 5.1%. To further confirm the overall accuracy of the segmentation, we visually inspected automatic segmentation results on OFT longitudinal sections at three different time points (see last row in Fig. 9). We found that in most places deviations between visual estimations of the location of a boundary and segmentation lines were smaller than 1 pixel (around 5 μm). The comparison of segmentation results on the 2-D cross-sections and longitudinal sections demonstrated that our segmentation strategy can accurately extract the OFT surfaces of the myocardium and lumen from 4-D OCT images of the developing heart even when images are noisy and intensity levels (as well as signal-to-noise ratio) diminish with depth.