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Segmentation of White Blood Cells in Acute Myeloid Leukemia Microscopic Images: A Review

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Prognostic Models in Healthcare: AI and Statistical Approaches

Part of the book series: Studies in Big Data ((SBD,volume 109))

Abstract

Acute Myeloid Leukemia (AML) is a fast-growing leukemia caused by the rapid proliferation of immature myeloid cells. AML is a life-threatening disease if left untreated. Therefore, early detection of AML is crucial, maximizes the cure opportunities, and saves patients’ lives. Initial AML diagnosis is done by expert pathologists where blood smear images are utilized to detect abnormalities in WBCs. However, manual detection of AML is subjective and prone to errors. On the contrary, computer-aided diagnosis (CAD) systems can be an accurate diagnostic tool for AML and assist pathologists during the diagnosis process. Segmentation of White Blood Cells is the first step toward developing an accurate CAD system for AML. To date, WBC segmentation has several challenges due to several reasons such as different staining conditions, complex nature of microscopic blood images, and morphological diversity of WBCs. Current WBC segmentation techniques vary from conventional image processing methods to advanced machine learning and deep learning methods. This chapter discusses current segmentation methods as well as the potential solutions for improving automated WBC segmentation accuracy.

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Authors and Affiliations

  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Tusneem A. Elhassan & Mohd Shafry Mohd Rahim

  2. Bioinspired Device and Tissue Engineering Research Group, Faculty of Engineering, School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, 81300, Skudai, Johor, Malaysia

    Tan Tian Swee

  3. Department of Data Science, Universiti Malaysia Kelantan, Kota Bharu, Malaysia

    Siti Z. Mohd Hashim

  4. King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia

    Mahmoud Aljurf

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  1. Tusneem A. Elhassan
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  2. Mohd Shafry Mohd Rahim
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  3. Tan Tian Swee
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  5. Mahmoud Aljurf
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Correspondence to Mohd Shafry Mohd Rahim .

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Editors and Affiliations

  1. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  2. Artificial Intelligence and Data Analytics Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Amjad Rehman

  3. Artificial Intelligence and Data Science, Jio Institute, Ulwe, Navi Mumbai, India

    Sudipta Roy

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Elhassan, T.A., Rahim, M.S.M., Swee, T.T., Hashim, S.Z.M., Aljurf, M. (2022). Segmentation of White Blood Cells in Acute Myeloid Leukemia Microscopic Images: A Review. In: Saba, T., Rehman, A., Roy, S. (eds) Prognostic Models in Healthcare: AI and Statistical Approaches. Studies in Big Data, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-19-2057-8_1

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