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Alveolar wall extraction is important to elucidate the 3D microstructure of alveolar ducts and alveolar sacs. The purpose of this study is to improve the accuracy and speed of alveolar wall extraction using U-Net, and to contribute to the analysis of the 3D lung microstructure. In our experiments, we first performed alveolar wall extraction using 2D U-Net. As a result, the accuracy rate and F-measure were 0.982 and 0.916, respectively. Next, the model created by 2D U-Net was tested on the axial, coronal, and sagittal planes of the 3D image, and the results were used to create training data for 3D U-Net by performing AND, Majority-Vote, and OR operations. The results of the Majority-Vote operation yielded an accuracy rate of 0.980 and an F-measure of 0.909, which was the best among the three operations. In addition to the metrics, we also evaluated the accuracy in terms of topology. The results of the 2D U-Net test were 12 connected components, 17 cavities, and 164 holes, while the results of the Majority-Vote operation were counted as 36 connected components, 35 cavities, and 105 holes. Since these numbers alone are insufficient to determine which is superior, a qualitative evaluation is also necessary. Qualitative evaluation showed that some images were more accurate as a result of Majority-Vote operation than as a result of 2D U-Net.
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