Dai Haoran, Tao Yubo, He Xiangyang, Lin Hai
State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China.
J Vis (Tokyo). 2021;24(6):1253-1266. doi: 10.1007/s12650-021-00770-2. Epub 2021 Aug 19.
The high-resolution scanning devices developed in recent decades provide biomedical volume datasets that support the study of molecular structure and drug design. Isosurface analysis is an important tool in these studies, and the key is to construct suitable description vectors to support subsequent tasks, such as classification and retrieval. Traditional methods based on handcrafted features are insufficient for dealing with complex structures, while deep learning-based approaches have high memory and computation costs when dealing directly with volume data. To address these problems, we propose IsoExplorer, an isosurface-driven framework for 3D shape analysis of biomedical volume data. We first extract isosurfaces from volume data and split them into individual 3D shapes according to their connectivity. Then, we utilize octree-based convolution to design a variational autoencoder model that learns the latent representations of the shape. Finally, these latent representations are used for low-dimensional isosurface representation and shape retrieval. We demonstrate the effectiveness and usefulness of IsoExplorer via isosurface similarity analysis, shape retrieval of real-world data, and comparison with existing methods.
近几十年来开发的高分辨率扫描设备提供了支持分子结构研究和药物设计的生物医学体积数据集。等值面分析是这些研究中的重要工具，关键在于构建合适的描述向量以支持后续任务，如分类和检索。基于手工特征的传统方法在处理复杂结构时不够充分，而基于深度学习的方法在直接处理体积数据时具有高内存和计算成本。为了解决这些问题，我们提出了IsoExplorer，这是一个用于生物医学体积数据三维形状分析的等值面驱动框架。我们首先从体积数据中提取等值面，并根据其连通性将它们分割成单个三维形状。然后，我们利用基于八叉树的卷积来设计一个变分自编码器模型，该模型学习形状的潜在表示。最后，这些潜在表示用于低维等值面表示和形状检索。我们通过等值面相似性分析、真实世界数据的形状检索以及与现有方法的比较，证明了IsoExplorer的有效性和实用性。
