Abstract
SIGNIFICANCE: Accurate cell classification is essential in disease diagnosis and drug screening. Three-dimensional (3D) voxel models derived from holographic tomography effectively capture the internal structural features of cells, enhancing classification accuracy. However, their high dimensionality leads to significant increases in data volume, computational complexity, processing time, and hardware costs, which limit their practical applicability. AIM: We aim to develop an efficient and accurate cell classification method using 3D refractive index (RI) point cloud data obtained from holographic tomography, focusing on reducing computational complexity without sacrificing classification performance. APPROACH: We transformed 3D RI voxel data into point cloud representations using segmented equilibrium sampling to substantially decrease data volume while retaining crucial structural features. A deep learning model, named RI-PointNet++, was then specifically designed for RI point cloud data to enhance feature extraction and enable precise cell classification. RESULTS: In experiments classifying the viability of HeLa cells, the proposed method achieved a classification accuracy of 93.5%, significantly outperforming conventional two-dimensional models (87.0%). Furthermore, compared with traditional 3D voxel-based models, our method reduced computational complexity by over 99%, with floating-point operations of only 1.49 G, thus enabling efficient performance even on central processing unit (CPU) hardware. CONCLUSIONS: Our proposed method provides an innovative, lightweight solution for 3D cell classification, highlighting the considerable potential of point cloud-based approaches in biomedical research applications.