Significance: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable.
Aim: An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization.
Approach: Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images.
Results: The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed.
Conclusions: The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.
Complete blood count is the most common test to detect anemia, but it is unable to obtain the abnormal shape of erythrocytes, which highly correlates with the hematologic function. Tomographic diffractive microscopy (TDM) is an emerging technique capable of quantifying three-dimensional (3-D) refractive index (RI) distributions of erythrocytes without labeling. TDM was used to characterize optical and morphological properties of 172 erythrocytes from healthy volunteers and 419 erythrocytes from thalassemic patients. To efficiently extract and analyze the properties of erythrocytes, we developed an adaptive region-growing method for automatically delineating erythrocytes from 3-D RI maps. The thalassemic erythrocytes not only contained lower hemoglobin content but also showed doughnut shape and significantly lower volume, surface area, effective radius, and average thickness. A multi-indices prediction model achieved perfect accuracy of diagnosing thalassemia using four features, including the optical volume, surface-area-to-volume ratio, sphericity index, and surface area. The results demonstrate the ability of TDM to provide quantitative, hematologic measurements and to assess morphological features of erythrocytes to distinguish healthy and thalassemic erythrocytes.
Three-dimensional (3D) refractive-index (RI) microscopy is an emerging technique suitable for live-cell imaging due to its label-free and fast 3D imaging capabilities. We have developed a common-path system to acquire 3D RI microscopic images of cells with excellent speed and stability. After obtaining 3D RI distributions of individual leukocytes, we used a 3D finite-difference time-domain tool to study light scattering properties. Backscattering spectra of lymphocytes, monocytes and neutrophils are different from each other. Backscattering spectra of lymphocytes matched well with those of homogeneous spheres as predicted by Mie theory while backscattering spectra of neutrophils are significantly more intense than those of the other two types. This suggests the possibility of classifying the three types of leukocytes based on backscattering.
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