The accurate classification of white blood cells (WBCs) is crucial in the examination of blood and the diagnosis and treatment of clinical conditions. Manual examination under a bright-field microscope, the gold standard for blood cell analysis, is time-consuming and inspector-dependent. Currently, blood cell analyzers based on the impedance method or flow cytometry are extensively employed. However, some false positives may occur because of the structural variability of WBCs, which requires a manual microscopic review. In addition, these instruments are expensive. Deep learning, which can reduce the technical requirements of inspectors, is widely used for WBC classification. However, this analysis continues to rely on the morphology and color characteristics of the stained cells. To achieve high accuracy in the classification of WBCs, the process usually requires image acquisition and processing under a 100× objective lens, which can be time-consuming and data-intensive. Quantitative-phase imaging (QPI) is an effective method for studying cell morphology and biochemistry. However, identifying WBCs solely based on their phase characteristics is challenging, particularly when these phase characteristics are not prominent. Research on stained cells using QPI has shown that the inclusion of phase information, alongside bright-field pictures, might provide useful insights for WBC classification. In this study, the phase distributions of five different types of WBCs were quantitatively analyzed, and the substructure phase information was effectively divided using a co-localization imaging system based on digital holographic microscopy (DHM) and bright-field microscopy. A series of feature parameters were extracted to assist with the WBC classification. The accuracies of the classification of the three types of granulocytes based on the extracted phase feature parameters were 94%. Additionally, atypical lymphocytes were studied, and a recognition accuracy of 84.5% was achieved. The proposed method utilizes routine blood smear samples stained for clinical microscopy, making it easy to integrate into a commercial microscopic system and providing a wide range of practical applications.
A benchtop co-localization imaging system was used to obtain bright-field images and quantitative phase images of WBCs from peripheral blood smears of healthy individuals. Quantitative phase images of the WBCs were reconstructed from off-axis holograms obtained from DHM. To segment the phase information, WBCs were first extracted and divided into two parts, the nucleus and the cytoplasm, based on bright-field images. Then, the position information of the nucleus and cytoplasm of the WBCs in the bright-field images was transposed onto the corresponding phase images. Finally, the quantitative phase distributions of WBCs and their corresponding nuclei and cytoplasm were successfully acquired. A substantial number of WBC samples consisting of 100 neutrophils, eosinophils, basophils, monocytes, large lymphocytes, and small lymphocytes were selected for co-localization imaging and statistical analysis. Various feature parameters were extracted to quantitatively describe and analyze the morphological and substructural features of the different WBCs.
The feature parameters of the five types of WBCs were subjected to analysis and comparison, revealing distinct phase characteristics for each type. Neutrophils had a substantially higher nuclear phase value than the cytoplasmic phase value [Fig. 4(a)], whereas eosinophils had comparable nuclear and cytoplasmic phase values (Fig. 4). The cytoplasmic phase values in basophils fluctuated substantially [Fig. 5(c)], and monocytes showed a smaller phase difference between the nucleus and cytoplasm than lymphocytes [Fig. 4(b)]. Using the extracted feature parameters, three types of granulocytes were successfully classified with 94% accuracy. The efficiency of classifying phase features was evaluated by analyzing a total of 1200 neutrophils and eosinophils. This analysis was conducted using a phase feature method based on a 40× co-localization microscope, deep learning classification based on a 40× brightfield microscope, and a commercial system called Morphogo with a 100× microscope. The results showed that the phase feature accurately identified easily confused cells in deep learning classification or the Morphogo system (Fig. 7). Furthermore, an examination of atypical cells was conducted, revealing that the use of phase characteristics resulted in a classification accuracy of 84.5%. These results demonstrate that the phase feature parameters are effective in aiding WBC classification.
This study proposes a method for classifying WBCs using QPI. The approach involved analyzing different types of WBCs using a co-localization imaging system that combines DHM and bright-field microscopy. The position and structural information of WBCs were obtained from bright-field images, and the phase information of WBCs and their nuclei and cytoplasm were extracted accordingly. Statistical analysis was then used to extract feature parameters that effectively aided in the classification of WBCs. This method achieved an accuracy rate of 94% for classifying the three types of granulocytes based on the substructure phase characteristic parameters. Further analysis showed an accuracy rate of 84.5% for identifying atypical lymphocytes, which are often misinterpreted during microscopic examinations. Compared with using only phase information to classify WBCs, the proposed method incorporates high contrast between the nucleus and cytoplasm in bright-field images to effectively compare the characteristics of different WBC substructures, leading to an improved classification scope and accuracy. In addition, compared to conventional microscopic classification, the proposed method provides additional phase information that can assist in WBC classification. This method is easy to integrate with microscope and does not require the special treatment of conventionally stained blood smear samples. It is expected to be widely used for the leukocyte classification and diagnosis and treatment of various blood diseases.
