用分层网格变分自编码器构建器官形状图谱，实现高精度可解释的三维形状建模。

An organ shape atlas, which represents the shape and position of the organs and skeleton of a living body using a small number of parameters, is expected to have a wide range of clinical applications, including intraoperative guidance and radiotherapy. Because the shape and position of soft organs vary greatly among patients, it is difficult for linear models to reconstruct shapes that have large local variations. Because it is difficult for conventional nonlinear models to control and interpret the organ shapes obtained, deep learning has been attracting attention in three-dimensional shape representation. In this study, we propose an organ shape atlas based on a mesh variational autoencoder (MeshVAE) with hierarchical latent variables. To represent the complex shapes of biological organs and nonlinear shape differences between individuals, the proposed method maintains the performance of organ shape reconstruction by hierarchizing latent variables and enables shape representation using lower-dimensional latent variables. Additionally, templates that define vertex correspondence between different resolutions enable hierarchical representation in mesh data and control the global and local features of the organ shape. We trained the model using liver and stomach organ meshes obtained from 124 cases and confirmed that the model reconstructed the position and shape with an average distance between vertices of 1.5 mm and mean distance of 0.7 mm for the liver shape, and an average distance between vertices of 1.4 mm and mean distance of 0.8 mm for the stomach shape on test data from 19 of cases. The proposed method continuously represented interpolated shapes, and by changing latent variables at different hierarchical levels, the proposed method hierarchically separated shape features compared with PCA.