TY - JOUR T1 - Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning JF - bioRxiv DO - 10.1101/2020.11.13.381244 SP - 2020.11.13.381244 AU - DongHun Ryu AU - Jinho Kim AU - Daejin Lim AU - Hyun-Seok Min AU - Inyoung You AU - Duck Cho AU - YongKeun Park Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/11/15/2020.11.13.381244.abstract N2 - In this study, we report a label-free bone marrow white blood cell classification framework that captures the three-dimensional (3D) refractive index (RI) distributions of individual cells and analyzes with deep learning. Without using labeling or staining processes, 3D RI distributions of individual white blood cells were exploited for accurate profiling of their subtypes. Powered by deep learning, our method used the high-dimensional information of the WBC RI tomogram voxels and achieved high accuracy. The results show >99 % accuracy for the binary classification of myeloids and lymphoids and >96 % accuracy for the four-type classification of B, T lymphocytes, monocytes, and myelocytes. Furthermore, the feature learning of our approach was visualized via an unsupervised dimension reduction technique. We envision that this framework can be integrated into existing workflows for blood cell investigation, thereby providing cost-effective and rapid diagnosis of hematologic malignancy.Competing Interest StatementH. Min and Y. Park are employees of Tomocube, Inc., which is one of the sponsors for this work. ER -