Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Nano Lett. 2023 Jun 14;23(11):5148-5154. doi: 10.1021/acs.nanolett.3c01077. Epub 2023 May 30.
Three-dimensional (3D) characterization of organisms is important for the study of cellular phenotypes, structural organization, and mechanotransduction. Existing optical techniques for 3D imaging rely on focus stacking or complex multiangle projection. Focus stacking has deleterious axial resolution due to the one-angle optical projection. Herein, we achieve high-resolution 3D imaging and classification of organisms based on standard optical microscopy coupled to optothermal rotation. Through a seamless fusion of optical trapping and rotation of organisms on a single platform, our technique is applicable to any organism suspended in clinical samples, enabling contact-free and biocompatible 3D imaging. Moreover, when applying deep learning to distinguish different types of biological cells with high similarity, we demonstrate that our platform improves the classification accuracy (96% vs 85%) while using one-tenth the number of training samples compared with conventional deep-learning-based classification.
三维（3D）生物体特征描述对于细胞表型、结构组织和力学转导的研究非常重要。现有的用于 3D 成像的光学技术依赖于聚焦堆叠或复杂的多角度投影。由于单一角度的光学投影，聚焦堆叠会对轴向分辨率造成损害。在此，我们通过将标准光学显微镜与光热旋转相结合，实现了基于生物体的高分辨率 3D 成像和分类。通过在单个平台上对生物体进行光学捕获和旋转的无缝融合，我们的技术适用于任何悬浮在临床样本中的生物体，实现了无接触和生物兼容的 3D 成像。此外，在应用深度学习来区分具有高相似度的不同类型的生物细胞时，我们证明与传统基于深度学习的分类相比，我们的平台在使用十分之一数量的训练样本的情况下，提高了分类准确性（96%对 85%）。
