Abstract
Standardized morphological evaluation in pathology is usually qualitative. Classifying and qualitatively analyzing the nucleated cells in the bone marrow aspirate images based on morphology is crucial for the diagnosis of acute myoid leukemia (AML), acute lymphoblastic leukemia (ALL), and Myelodysplastic syndrome (MDS), etc. However, it is time-consuming and difficult to accurately identify nucleated cells and calculate the percentage of the cells because of the complexity of bone marrow aspirate images. This paper proposed a deep learning analysis model of bone marrow aspirate images, termed Cell Detection and Confirmation Network (CDC-NET), for the aided diagnosis of AML by improving the accuracy of cell detection and recognition. Specifically, we take the nucleated cells in the bone marrow aspirate images as the detection objects to establish the model. Since some cells from different categories have similar morphology, classification error is inevitable. We design a confirmation network in which multiple trained classifiers work as pathologists to confirm the cell category by a voting method. To demonstrate the effectiveness of the proposed approach, experiments on clinical microscopic datasets are conducted. The Recall and Precision of CDC-NET are 78.54% and 91.74% respectively, and the missed rate of our method is lower than those of the other popular methods. The experimental results demonstrated that the proposed model has the potential for the pathological analysis of aspirate smears and the aided diagnosis of AML.
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The data used to support the findings of this study are available from the corresponding author upon request.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (No. 52001039), National Natural Science Foundation of China under Grand (No.52171310), Shandong Natural Science Foundation in China (No. ZR2019LZH005), Research fund from Science and Technology on Underwater Vehicle Technology Laboratory (No.2021JCJQ-SYSJJ-LB06903). University Innovation Team Project of Jinan (No.2019GXRC015). Science and technology improvement project for small and medium-sized enterprises in Shandong Province (No. 2021TSGC1012).
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Highlights
• A Cell Detection and Confirmation Network is designed to achieve automatic analysis of the bone marrow aspirate images to assist in AML diagnosis.
• It simulates pathologists by introducing a voting mechanism to balance their perspectives and further achieve consistent results, and it possesses excellent scalability.
• The experimental results demonstrated that the proposed model has the potential for the pathological analysis of aspirate smears and the aided diagnosis of AML.
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Su, J., Liu, Y., Zhang, J. et al. CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML. Med Biol Eng Comput 62, 575–589 (2024). https://doi.org/10.1007/s11517-023-02955-3
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DOI: https://doi.org/10.1007/s11517-023-02955-3