Blood cell counting based on U-Net++ and YOLOv5
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1. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China;2.Shenzhen Weian Co-create Biotechnology Co., Ltd., Shenzhen 518000, China;3. Tianjin Key Laboratory of Engineering Technologies for Cell Phamaceutical, Tianjin 300457, China

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    Abstract:

    Clinical information about a variety of disorders is available through blood cell counting, which is usually done by manual methods. However, manual methods are complex, time-consuming and susceptible to the subjective experience of inspectors. Although many efforts have been made to develop automated blood cell counting algorithms, the complexity of blood cell distribution and the highly overlapping nature of some red blood cells (RBCs) remain significant challenges that limit the improvement of analytical accuracy. Here, we proposed an end-to-end method for blood cell counting based on deep learning. Firstly, U-Net++ was used to segment the whole blood cell image into several regions of interest (ROI), and each ROI contains only one single cell or multiple overlapping cells. Subsequently, YOLOv5 was used to detect blood cells in each ROI. Specifically, we proposed several strategies, including fine classification of RBCs, adaptive adjustment for non-maximal suppression (NMS) threshold and blood cell morphology constraints to improve the accuracy of detection. Finally, the detection outcomes for each ROI were combined and superimposed. The results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells, with a 98.18% accuracy rate for blood cell counting.

    Reference
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BAI Hua, WANG Xuechun, GUAN Yingjian, GAO Qiang, HAN Zhibo. Blood cell counting based on U-Net++ and YOLOv5[J]. Optoelectronics Letters,2023,19(6):370-376

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History
  • Received:October 08,2022
  • Revised:January 18,2023
  • Online: June 16,2023
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