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ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deep learning

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Abstract

The detection, identification and counting of bone marrow erythroid cells are vital for evaluating the health status and therapeutic schedules of patients with leukemia or hematopathy. However, traditional methods used in hospitals are still based on chemical reagent staining, manual detection and counting with the help of laboratory equipment. And therefore, these methods are time-consuming, laborious, and tedious. The development of deep learning in the field of image processing makes it possible for effective automated detection and classification of erythroid cells. In this research, we proposed a pipeline called ErythroidCounter, which is based on deep learning approaches to perform fully automated detection and classification of erythroid cells. ErythroidCounter is composed of the detection and extraction module (DEM) followed by classification and counting module (CCM). DEM adapts RetinaNet to locate and detect erythroid cells, and it transmits the detected cell images into CCM, while CCM is based on the DenseNet-121 architecture to perform classification and counting., which has close match in terms of classification accuracy compared to manual examination. When classifying erythroid cells, the ErythroidCounter achieved an accuracy of 86.33%, recall of 87.45%, precision of 87.16%, and F1 score of 87.30%. When detecting erythroid cells, ErythroidCounter achieved an precision of 90.7%, recall of 91.3%, and F1 score of 90.9%. EythroidCounter is robust to underlying color images, cell densities, and cell positions. To the best of our knowledge, this is the first automatic approach for erythroid cell detection, classification, and counting in real clinical scenarios, and it can be used as an assistive tool for medical examinations.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grants Nos. 61772227, 61972174, 61972175), Science and Technology Development Foundation of Jilin Province (No. 20180201045GX, 20200201300JC, 20200401083GX, 20200201163JC), the Jilin Development and Reform Commission Fund (No. 2020C020-2).

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Authors and Affiliations

  1. Jilin University, 2699 Qianjin Street, Changchun, China

    You Zhou, Ye Wang, Junhui Wu, Muhammad Hassan & Liupu Wang

  2. Heriot-Watt University, Edinburgh, Scotland, UK

    Wei Pang

  3. The Second Hospital of Jilin University, Changchun, China

    Lili Lv & Honghua Cui

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  1. You Zhou
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  2. Ye Wang
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  3. Junhui Wu
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  4. Muhammad Hassan
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The original online version of this article was revised: The corresponding author "You Zhou" in the original publication of this article was incorrect. The corresponding author should be "Liupu Wang".

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Zhou, Y., Wang, Y., Wu, J. et al. ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deep learning. Multimed Tools Appl 81, 25541–25556 (2022). https://doi.org/10.1007/s11042-022-12209-3

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