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BCNet: integrating UNet and transformer for blood cell segmentation

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Abstract

Automatic segmentation of blood cells is crucial in medical diagnosis and research, significantly improving the accuracy and efficiency of diagnosing blood disorders. Traditional segmentation methods involving manual segmentation are time-consuming, labor-intensive, and prone to errors. In recent years, advancements in deep learning have provided new solutions for automated segmentation. This paper proposes BCNet, a blood cell segmentation algorithm combining UNet and Transformer. Specifically, BCNet utilizes UNet’s Encoder-Decoder architecture as the backbone for extracting multi-scale features. A Spatial Reduction Transformer (SRT) Module is introduced for capturing long-range dependencies in the deepest downsampling layers to enhance sensitivity to local features. Additionally, coordinate attention is employed instead of skip connections for multi-scale feature fusion, enriching semantic information in deep features. Experimental results demonstrate that BCNet achieves superior Dice and IoU metrics compared to classical medical image segmentation models, facilitating automated analysis and medical diagnosis of blood cells.

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Data availability statement

This work was supported in Tianjin Research Innovation Project for Postgraduate Students (2022SKY126).

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Funding

This work was supported by JSPS KAKENHI Grant Numbers JP21H05052, JP21K11881, JP23H00479, JP24K02938, and JST, CREST Grant Number JPMJCR22M1, Japan. This work was also supported by DIGIT Aarhus University Centre for Digitalisation, Big Data and Data Analytics, and Digital Research Centre Denmark (DIREC) under the Privacy and Machine Learning project, Denmark.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China

    Yue Jiang, Shuaidan Wang, Minjie Yao, Qing Xiao, Hua Bai & Zhuo Zhang

  2. Law school, Civil Aviation University of China, Tianjin, 300300, China

    Yinghui Li

  3. College of Computer Science, National University of Defense Technology, Changsha, 410073, China

    Zhuo Zhang

Authors
  1. Yue Jiang
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  2. Shuaidan Wang
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  3. Minjie Yao
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  4. Qing Xiao
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  5. Yinghui Li
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  6. Hua Bai
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  7. Zhuo Zhang
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Contributions

Conceptualization, Z.Z. and Y.J.; methodology, H.B.; software, Y.J.; validation, Y.L., H.B. and Z.Z.; formal analysis, S.W.; investigation, M.Y.; resources, Q.X.; data curation, Z.Z.; writing-original draft preparation, Y.J.; writing-review and editing, H.B. and Y.L.; visualization, Y.J.; supervision, Z.Z.; project administration, H.B.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Hua Bai or Zhuo Zhang.

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The authors declare that they have no Conflict of interest.

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The study utilized publicly available datasets, and therefore, ethical review and approval were not required in accordance with the local legislation and institutional requirements.

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The study utilized publicly available datasets, and therefore, Consent to Participate and Consent to Publish were not required in accordance with the local legislation and institutional requirements.

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Jiang, Y., Wang, S., Yao, M. et al. BCNet: integrating UNet and transformer for blood cell segmentation. SIViP 19, 14 (2025). https://doi.org/10.1007/s11760-024-03568-5

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