A deep learning method for counting white blood cells in bone marrow images

• Main Authors: Chang, H.C (Author), Ding, K. (Author), Hwang, K.-S (Author), Hwang, M. (Author), Jiang, W.-C (Author), Wang, D. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Blood; Bone; bone marrow; Bone marrow; Bone Marrow; Bone marrow images; Cell/B.E; Cell/BE; Cell-be; Cells; Classification; Cytology; Deep learning; Deep Learning; Different class; Diseases; human; Humans; Image classification; image processing; Image Processing, Computer-Assisted; Learning methods; leukemia; Leukemia; leukocyte; Leukocytes; Medical image; Medical imaging; Object detection; Statistical tests; User interfaces; White blood cells
• Online Access: View Fulltext in Publisher

 Background: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counting the different classes of WBC is time-consuming and fatiguing. Classification and counting accuracy depends on the capability and experience of operators. Results: This paper uses a deep learning method to count cells in color bone marrow microscopic images automatically. The proposed method uses a Faster RCNN and a Feature Pyramid Network to construct a system that deals with various illumination levels and accounts for color components' stability. The dataset of The Second Affiliated Hospital of Zhejiang University is used to train and test. Conclusions: The experiments test the effectiveness of the proposed white blood cell classification system using a total of 609 white blood cell images with a resolution of 2560 × 1920. The highest overall correct recognition rate could reach 98.8% accuracy. The experimental results show that the proposed system is comparable to some state-of-art systems. A user interface allows pathologists to operate the system easily. © 2021, The Author(s).