A rapid white blood cell classification system based on multimode imaging technology

Abstract

In order to simplify the complexity of white blood cell classification in existing point-of-care testing (POCT) testing equipment, a white blood cell classification detection system based on microfluidic and multimode imaging was constructed. Microfluidic chip was used in the system. A multimodal optical imaging system based on the characteristics of blood samples was designed to obtain eigenvalue extraction of cells. Afterward, a BP neural network model was constructed to realize automatic classification of white blood cells. Finally, 80 human blood samples were classified and detected by this system and compared with the results of Sysmex XE-5000. The consistency correlation coefficients of white blood cells, lymphocytes, monocytes, neutrophils and eosinophils are 1.038, 0.907, 0.549, 0.922 and 1.028, respectively, and the CV values of the four types of white blood cells in the stability test were all below 10%. In this study, a white blood cell classification and detection system with small size, simple operation, fast single-sample detection, high accuracy, and no maintenance is required. It will provide a solid technical support for the further development of POCT blood cell analysis equipment.

StainGAN: Learning a structural preserving translation for white blood cell images

“…In this sense, rapid staining would be superior that the unnecessary distractions from the richer details within WBCs and the RBCs are suppressed. However, there are two challenges that remain inadequately addressed: (1) The rapid staining procedure is somewhat unstable, which may result in dyeing impurities and color variations. (2) The rapidly-stained WBC images are relatively scarce because the procedure is less popular compared to traditional staining methods, perhaps partially due to the fact that the reagent formula is not yet made public.…”

Ginsenosides are active ingredients in Panax ginseng with immunomodulatory properties from cellular to organismal levels

“…They are produced by the thymus and contain two main types, granulocytes and agranulocytes. Granulocytes include neutrophils, eosinophils and basophils, and agranulocytes include lymphocytes and monocytes [ 35 ]. Neutrophils are the major component of our white blood cells and play a role as body scavengers that get rid of many bacteria and fungus.…”

Automatic detection of three cell types in a microscope image based on deep learning

“…In 2020, Alqudah et al proposed an object detection algorithm based on deep learning [24], which uses the cell images of human blood smear images for malaria parasite detection and achieved relatively good cell classification detection. Lv et al [25] designed a multimodal optical imaging system based on the characteristics of blood samples to obtain eigenvalue extraction of cells. In 2021, Rong et al [26] proposed a Mask R-CNN-based object detection algorithm for erythrocytes.…”