Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

Objective and Impact Statement.We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index(RI)tomography.Our computational approach that fully utilizes tomographic information of bone marrow(BM)white blood cell(WBC)enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research.Introduction.Conventional methods for examining blood cells,such as blood smear analysis by medical professionals and fluorescence-activated cell sorting,require significant time,costs,and domain knowledge that could affect test results.While label-free imaging techniques that use a specimen’s intrinsic contrast(e.g.,multiphoton and Raman microscopy)have been used to characterize blood cells,their imaging procedures and instrumentations are relatively time-consuming and complex.Methods.The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network.We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors(n=10):monocyte,myelocyte,B lymphocyte,and T lymphocyte.The quantitative parameters of WBC are directly obtained from the tomograms.Results.Our results show>99%accuracy for the binary classification of myeloids and lymphoids and>96%accuracy for the four-type classification of B and T lymphocytes,monocyte,and myelocytes.The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique.Conclusion.We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows,providing cost-effective and rapid diagnosis for hematologic malignancy.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections.Microbial infections are a major healthcare issue worldwide,as these widespread diseases often develop into deadly symptoms.While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection,this effective treatment is difficult to practice.The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification,which includes time-consuming sample growth.Here,we propose a microscopy-based framework that identifies the pathogen from single to few cells.Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network.We demonstrate the identification of 19 bacterial species that cause bloodstream infections,achieving an accuracy of 82.5%from an individual bacterial cell or cluster.This performance,comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample,underpins the effectiveness of our framework in clinical applications.Furthermore,our accuracy increases with multiple measurements,reaching 99.9%with seven different measurements of cells or clusters.We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

Three-dimensional label-free morphology of CD8+T cells as a sepsis biomarker

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management.We aimed to investigate the potential of three-dimensional label-free CD8+T cell morphology as a biomarker for sepsis.This study included three-time points in the sepsis recovery cohort(N=8)and healthy controls(N=20).Morphological features and spatial distribution within cells were compared among the patients'statuses.We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology.Correlation between the morphological features and clinical indices were analysed.Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups.The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100%with only a few cells,and a strong correlation between the morphological features and clinical indices was observed.Our study highlights the potential of three-dimensional label-free CD8+T cell morphology as a promising biomarker for sepsis.This approach is rapid,requires a minimum amount of blood samples,and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.