In this study, a novel method with the U-Net-based network architecture, 2D U-Net, is employed to segment the position of lung nodules, which are an early symptom of lung cancer and have a high probability of becoming a carcinoma, especially when a lung nodule is bigger than 15 [Formula: see text]. A serious problem of considering deep learning for all medical images is imbalanced labeling between foreground and background. The lung nodule is the foreground which accounts for a lower percentage in a whole image. The evaluation function adopted in this study is dice coefficient loss, which is usually used in image segmentation tasks. The proposed pre-processing method in this study is to use complementary labeling as the input in U-Net. With this method, the labeling is swapped. The no-nodule position is labeled. And the position of the nodule becomes non-labeled. The result shows that the proposal in this study is efficient in a small quantity of data. This method, complementary labeling could be used in a small data quantity scenario. With the use of ROI segmentation model in the data pre-processing, the results of lung nodule detection can be improved a lot as shown in the experiments.