Fig 2. Shows the workflow diagram of the point cloud and PointNet++ for pattern analysis of GGO distribution.

The PointNet++ architecture used in this study is the same as the original architecture in [28 and 29]. The data preparation pipeline is new, which we developed for generating point clouds from 3D lung CT scans. We stacked all segmented lung images to construct a 3D image and used sampling and grouping to select 2048 points for point clouds. We fed those points to the PointNet++ architecture, which was constructed with a multiple set of abstraction layers. Each set abstraction layer consisted of a sampling layer, grouping layer, and PointNet [28]. Multiple set abstraction layers formed a hierarchical feature learning architecture, which was divided into two parts: Segmentation and classification. For data segmentation, we merged local and global features to get the score of individual data points. For classification, global features were fed to two fully connected (FC) layers. MLP—Multilayer Perceptron.