Multi-Scale Prediction Network for Lung Segmentation

Abstract

Lung area segmentation is an essential step for disease analysis on thoracic computed tomography (CT) scans, which provide helpful information in visual inspection by physicians as well as in quantitative analysis by computer. In this work, we investigated deep convolutional neural network (DCNN) for lung area segmentation. With U-Net [1] and V-Net [2] (which can be regarded as a 3D version of U-Net) as the baseline network, a new multi-scale prediction network (MPN) was designed and evaluated with dice coefficient, Jaccard index, which is also known as Intersect over Union (IoU), Hausdorff distance and coverage rate of nodule areas as criteria. It was found that MPN achieved dice coefficient, Jaccard index, Hausdorff distance, and coverage rate of 0.9845, 0.9697, 3.7480, and 87.54% while the corresponding values for baseline network were 0.9830, 0.9669, 3.9381, and 85.18% (U-Net) and 0.9783, 0.9582, 4.4461 and 52.26% (V-Net).