Computers in biology and medicine
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Accurate segmentation of the left ventricle (LV) from cine magnetic resonance imaging (MRI) is an important step in the reliable assessment of cardiac function in cardiovascular disease patients. Several deep learning convolutional neural network (CNN) models have achieved state-of-the-art performances for LV segmentation from cine MRI. However, most published deep learning methods use individual cine frames as input and process each frame separately. ⋯ The results showed that OF-net achieves an average perpendicular distance (APD) of 0.90±0.08 pixels and a Dice index of 0.95±0.03 for LV segmentation in the middle slices, outperforming the classical U-net model (APD 0.92±0.04 pixels, Dice 0.94±0.16, p < 0.05). Specifically, the proposed method enhances the temporal continuity of segmentation at the apical and basal slices, which are typically more difficult to segment than middle slices. Our work exemplifies the ability of CNN to "learn" from expert experience when applied to specific analysis tasks.