Skip to main content
SpringerLink
Log in

CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

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.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Bruneau J, Molina TJ (2020) WHO Classification of tumors of hematopoietic and lymphoid tissues. In: Molina TJ (eds) Hematopathology. Encyclopedia of Pathology. Springer, Cham, pp 501–505. https://doi.org/10.1007/978-3-319-95309-0_3817

  2. Bonnet D, Dick JE (1997) Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med 3(7):730–737

    Article  CAS  PubMed  Google Scholar 

  3. Porcu S, Loddo A, Putzu L, Di Ruberto C (2018) White blood cells counting via vector field convolution nuclei segmentation. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (4):227–234

  4. Das DK, Maiti AK, Chakraborty C (2018) Automated identification of normoblast cell from human peripheral blood smear images. J Microsc 269(3):310–320

    Article  CAS  PubMed  Google Scholar 

  5. Ghribi O, Maalej A, Sellami L, Slima MB, Maalej MA, Mahfoudh KB, Dammak M, Mhiri C, Hamida AB (2019) Advanced methodology for multiple sclerosis lesion exploring: Towards a computer aided diagnosis system. Biomed Signal Process Control 49:274–288

    Article  Google Scholar 

  6. Matek C, Schwarz S, Spiekermann K, Marr C (2019) Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat Mach Intell 1(11):538–544

    Article  Google Scholar 

  7. El Alaoui Y, Elomri A, Qaraqe M, Padmanabhan R, YasinTaha R, El Omri H, El Omri A, Aboumarzouk O (2022) A review of artificial intelligence applications in hematology management: current practices and future prospects. J Med Internet Res 24(7):e36490

    Article  PubMed  PubMed Central  Google Scholar 

  8. Raina R, Gondhi NK, Chaahat R, Singh D, Kaur M, Lee HN (2023) A systematic review on acute leukemia detection using deep learning techniques. Arch Comput Methods Eng 30(1):251–270

    Article  Google Scholar 

  9. Saleem S, Amin J, Sharif M, Mallah GA, Kadry S, Gandomi AH (2022) Leukemia segmentation and classification: A comprehensive survey. Comput Biol Med 150:106028

    Article  PubMed  Google Scholar 

  10. Akram N, Adnan S, Asif M, Imran SMA, Yasir MN, Naqvi RA, Hussain D (2022) Exploiting the multiscale information fusion capabilities for aiding the leukemia diagnosis through white blood cells segmentation. IEEE Access 10:48747–48760

    Article  Google Scholar 

  11. Mustaqim T, Fatichah C, Suciati N (2023) Deep learning for the detection of acute lymphoblastic leukemia subtypes on microscopic images: a systematic literature review. IEEE Access (11):16108–16127. https://doi.org/10.1109/ACCESS.2023.3245128

  12. Li N, Fan L, Xu H, Zhang X, Bai Z, Li M, Xiong S, Jiang L, Yang J, Chen S, Qiao Y (2023) An AI-Aided diagnostic framework for hematologic neoplasms based on morphologic features and medical expertise. Lab Invest 103(4):100055

    Article  PubMed  Google Scholar 

  13. Goutam D, Sailaja S (2015) Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier. In: 2015 IEEE International Conference on Engineering and Technology (ICETECH). IEEE, pp 1–5

  14. Li Y, Zhu R, Mi L, Cao Y, Yao D (2016) Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method. Comput Math Methods Med 2016:9514707

    Article  PubMed  PubMed Central  Google Scholar 

  15. Aris TA, Nasir AA, Mustafa WA (2018) Analysis of distance transforms for watershed segmentation on chronic leukaemia images. J Telecommun Electron Comput Eng (JTEC) 10(1–16):51–56

    Google Scholar 

  16. Song TH, Sanchez V, Daly HE, Rajpoot NM (2018) Simultaneous cell detection and classification in bone marrow histology images. IEEE J Biomed Health Inform 23(4):1469–1476

    Article  PubMed  Google Scholar 

  17. Yang S, Liu X, Zheng Z, Wang W, Ma X (2021) Fusing medical image features and clinical features with deep learning for computer-aided diagnosis. arXiv preprint arXiv:2103.05855, 2021

  18. Fan H, Zhang F, Xi L, Li Z, Liu G, Xu Y (2019) LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J Biophotonics 12(7):e201800488

    Article  PubMed  Google Scholar 

  19. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  20. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  21. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  22. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448–456. pmlr

  23. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  24. Yu W, Chang J, Yang C, Zhang L, Shen H, Xia Y, Sha J (2017) Automatic classification of leukocytes using deep neural network. In: 2017 IEEE 12th international conference on ASIC (ASICON). IEEE, pp 1041–1044

  25. Suriya M, Chandran V, Sumithra MG (2022) Enhanced deep convolutional neural network for malarial parasite classification. Int J Comput Appl 44(12):1113–1122

    Google Scholar 

  26. Zerouaoui H, Idri A, Nakach FZ, Hadri RE (2021) Breast fine needle cytological classification using deep hybrid architectures. In: Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part II 21. Springer International Publishing, pp 186–202

  27. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(06):1137–1149

    Article  PubMed  Google Scholar 

  28. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, pp 21–37

  29. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

  30. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271

  31. Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. https://doi.org/10.48550/arXiv.1804.02767

  32. Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. https://doi.org/10.48550/arXiv.2004.10934

  33. Su J, Han J, Song J (2021) A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders. Comput Med Imaging Graph 90:101912

    Article  PubMed  Google Scholar 

  34. Rahman, MA, Wang, Y (2016) Optimizing intersection-over-union in deep neural networks for image segmentation. In: Bebis G, et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science, vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_22

  35. Raghavan V, Bollmann P, Jung GS (1989) A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans Inf Syst (TOIS) 7(3):205–229

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, China

    Jie Su, Jing Zhang & Jinjun Han

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

    Jie Su

  3. School of Information Management, Beijing Information Science & Technology University, Beijing, China

    Yahui Liu

  4. Department of Hematopathology and Lab Medicines, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Jinming Song

Authors
  1. Jie Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yahui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinjun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinming Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Su.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02955-3

Keywords

Search

Navigation