Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study
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IMAGE & SIGNAL PROCESSING
Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study Hong Jin 1 & Xinyan Fu 2 & Xinyi Cao 2 & Mingxia Sun 2 & Xiaofen Wang 1 & Yuhong Zhong 1 & Suwen Yang 1 & Chao Qi 1 & Bo Peng 2 & Xin He 3 & Fei He 3 & Yongfang Jiang 3 & Haiyan Gao 1,3 & Shun Li 4 & Zhen Huang 4 & Qiang Li 4 & Fengqi Fang 5 & Jun Zhang 1 Received: 15 July 2020 / Accepted: 25 August 2020 # The Author(s) 2020
Abstract Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders. Keywords Bone marrow smear . Differential cell count . Cell classification . Digital image
Introduction The incidence of hematopoietic and lymphoid malignancies is increasing worldwide. [1] Bone marrow (BM) aspirate
examination is a critical step in the initial work-up for hematological diseases. Differential counts of BM cells are requisite for diagnosis since the World Health Organization’s (WHO) classification of hematologic neoplasms relies on percentages
This article is part of the Topical Collection on Image & Signal Processing Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10916-020-01654-y) contains supplementary material, which is available to authorized users. * Xinyan Fu [email protected]
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Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou 311121, China
* Haiyan Gao [email protected]
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